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o,l=Dm(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Dm(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let A=cc(h,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:m,attrs:f,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,g);let x=g.map(w=>{if(w.rank!=null)return w;let{dataId:b,shape:v,dtype:N}=w;return this.makeTensorFromDataId(b,v,N)});if(a){let w=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(w)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(A=>this.keep(this.clone(A))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,A),A}}let{inputs:u,attrs:d}=e,p=Dm(e)?null:e.backwardsFunc,c;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(c=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(c),t=c.outputs)}),a&&this.addTapeNode(l,u,t,p,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:c.timeMs,extraInfo:c.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=Sm(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*xm(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ju||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*xm(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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r=0;r{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(r instanceof Le,()=>"The result y returned by f() must be a tensor.");let s=dS(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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BrowserFiles supports loading Keras-style tf.Model artifacts only.`),r.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],a=t.map(s=>ab(s.name)),r={};for(let s of e)s.paths.forEach(i=>{let o=ab(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${o}'`);if(n.push(o),a.indexOf(o)===-1)throw new Error(`Weight file with basename '${o}' is not provided.`);r[i]=t[a.indexOf(o)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return r}},uN=e=>ee().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(dl.URL_SCHEME)?dN(e.slice(dl.URL_SCHEME.length)):null;Nt.registerSaveRouter(uN);function dN(e="model"){return new dl(e)}function pN(e){return new lN(e)}function cb(e,t,n,a){i(e),n=n==null?0:n,a=a==null?1:a,o(n,a);let r=0,s=l=>(l.then(u=>{let d=n+ ++r/e.length*(a-n);return t(d),u}),l);function i(l){F(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,u){F(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),F(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),F(u>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${u}`)}return Promise.all(e.map(s))}async function hb(e,t){t==null&&(t={});let n=t.fetchFunc==null?ee().platform.fetch:t.fetchFunc,a=e.map(u=>n(u,t.requestInit,{isBinary:!0})),r=0,s=.5,i=(t.onProgress==null?await Promise.all(a):await cb(a,t.onProgress,r,s)).map(u=>u.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await cb(i,t.onProgress,o,l)}async function cN(e,t="",n,a){return fb(r=>hb(r,{requestInit:a}))(e,t,n)}function fb(e){return async(t,n="",a)=>{let r=t.map(()=>!1),s={},i=a!=null?a.map(()=>!1):[],o=[];if(t.forEach((h,m)=>{let f=0;h.weights.forEach(A=>{let y="quantization"in A?A.quantization.dtype:A.dtype,g=zm[y]*Et(A.shape),x=()=>{r[m]=!0,s[m]==null&&(s[m]=[]),s[m].push({manifestEntry:A,groupOffset:f,sizeBytes:g})};a!=null?a.forEach((w,b)=>{w===A.name&&(x(),i[b]=!0)}):x(),o.push(A.name),f+=g})}),!i.every(h=>h)){let h=a.filter((m,f)=>!i[f]);throw new Error(`Could not find weights in manifest with names: ${h.join(", ")}. 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u={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s},p=D.runKernel(wu,u,d);return p=me(p,o.dtype),l?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var pA=O({avgPool3d_:TT});function ET(e,t=0){F(e.length>=1,()=>"Pass at least one tensor to concat");let n=Gu(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor with dtype ${s.dtype}. `)}),n.length===1)return Pa(n[0]);let a=n,r={axis:t};return D.runKernel(fo,a,r)}var ot=O({concat_:ET});function CT(e){let t={x:M(e,"x","sigmoid")};return D.runKernel(Ks,t)}var Sn=O({sigmoid_:CT});function RT(e,t,n){let a=M(e,"x","slice","string_or_numeric");if(a.rank===0)throw new Error("Slicing scalar is not possible");let r={x:a},s={begin:t,size:n};return D.runKernel(Xo,r,s)}var Re=O({slice_:RT});function MT(e){let t={x:M(e,"x","tanh")};return D.runKernel(ni,t)}var fi=O({tanh_:MT});function FT(e,t,n,a,r,s){let i=M(e,"forgetBias","basicLSTMCell"),o=M(t,"lstmKernel","basicLSTMCell"),l=M(n,"lstmBias","basicLSTMCell"),u=M(a,"data","basicLSTMCell"),d=M(r,"c","basicLSTMCell"),p=M(s,"h","basicLSTMCell"),c=ot([u,p],1),h=Be(c,o),m=oe(h,l),f=m.shape[0],A=m.shape[1]/4,y=[f,A],g=Re(m,[0,0],y),x=Re(m,[0,A],y),w=Re(m,[0,A*2],y),b=Re(m,[0,A*3],y),v=oe(P(Sn(g),fi(x)),P(d,Sn(oe(i,w)))),N=P(fi(v),Sn(b));return[v,N]}var $T=O({basicLSTMCell_:FT});function DT(e,t,n){let a=M(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);F(a.rank>=1+t.length,()=>`input rank is ${a.rank} but should be > than blockShape.length ${t.length}`),F(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),F(a.shape[0]%r==0,()=>`input tensor batch is ${a.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let s={x:a},i={blockShape:t,crops:n};return D.runKernel(ku,s,i)}var Ju=O({batchToSpaceND_:DT});function zT(e){let t;return e.rank===0||e.rank===1?t=H(e,[1,1,1,e.size]):e.rank===2?t=H(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function OT(e,t,n,a,r,s){s==null&&(s=.001);let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;r!=null&&(u=M(r,"scale","batchNorm"));let d;a!=null&&(d=M(a,"offset","batchNorm")),F(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),F(d==null||o.rank===d.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),F(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let p={x:zT(i),scale:u,offset:d,mean:o,variance:l},c={varianceEpsilon:s},h=D.runKernel(Ss,p,c);return H(h,i.shape)}var mi=O({batchNorm_:OT});function _T(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;r!=null&&(u=M(r,"scale","batchNorm"));let d;return a!=null&&(d=M(a,"offset","batchNorm")),F(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),F(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),F(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),d!=null&&F(d.rank===2||d.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${d.rank}.`),mi(i,o,l,d,u,s)}var Lb=O({batchNorm2d_:_T});function PT(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;r!=null&&(u=M(r,"scale","batchNorm"));let d;return a!=null&&(d=M(a,"offset","batchNorm")),F(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),F(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),F(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),d!=null&&F(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${d.rank}.`),mi(i,o,l,d,u,s)}var Wb=O({batchNorm3d_:PT});function LT(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;r!=null&&(u=M(r,"scale","batchNorm"));let d;return a!=null&&(d=M(a,"offset","batchNorm")),F(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),F(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),d!=null&&F(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${d.rank}.`),mi(i,o,l,d,u,s)}var Bb=O({batchNorm4d_:LT});function WT(e,t,n){let a=M(e,"x","bincount"),r=M(t,"weights","bincount");F(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(r.size===a.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${a.shape}, weights shape: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return D.runKernel(Op,s,i)}var cA=O({bincount_:WT});function BT(e,t){let n=M(e,"broadcastTo","x"),a=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.lengthn.rank){let l=n.shape.slice();for(;l.length=0;l--)if(r[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to 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i=M(e,"x","dilation2d"),o=M(t,"filter","dilation2d");F(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),F(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),F(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,u=!1;i.rank===3&&(l=H(i,[1,i.shape[0],i.shape[1],i.shape[2]]),u=!0);let d={x:l,filter:o},p={strides:n,pad:a,dilations:r},c=D.runKernel(Nu,d,p);return u?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var yA=O({dilation2d_:uE});function dE(e,t){let n=e.length,a=[];for(let r=0;r1&&i===1&&a.unshift(s)}return a}function Wt(e,t){let n=[];for(let a=0;a1)&&n.unshift(s)}return n}function pt(e,t){let n=[],a=Math.max(e.length,t.length);for(let r=0;r`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${a.rank}.`);let r=n.rank===1?n.size:n.shape[1],s=a.rank===1?a.size:a.shape[0];if(F(r===s,()=>`Error in dot: inner dimensions of 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n=M(e,"x","expandDims","string_or_numeric");F(t<=n.rank,()=>"Axis must be <= rank of the tensor");let a={input:n},r={dim:t};return D.runKernel(vo,a,r)}var dn=O({expandDims_:bE});function vE(e){let t={x:M(e,"x","expm1")};return D.runKernel(wo,t)}var bA=O({expm1_:vE});function wE(e,t){let n=M(e,"x","tile","string_or_numeric");F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let a={x:n},r={reps:t};return D.runKernel(Cr,a,r)}var _r=O({tile_:wE});function kE(e,t,n,a="float32"){t==null&&(t=e);let r=We([e,t],a),s=e<=t?e:t;for(let o=0;o`Error in localResponseNormalization: x must be rank 3 or 4 but got rank ${s.rank}.`),F(Ht(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=H(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:i},u={depthRadius:t,bias:n,alpha:a,beta:r},d=D.runKernel(Ru,l,u);return o?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var kA=O({localResponseNormalization_:zE});function OE(e){let t={x:M(e,"x","log")};return D.runKernel(Cs,t)}var zn=O({log_:OE});function _E(e){let t={x:M(e,"x","log1p")};return D.runKernel(Fo,t)}var Fc=O({log1p_:_E});function PE(e){return F(Nr(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let a=M(t,"x","tf.grad","string_or_numeric"),r=n!=null?M(n,"dy","tf.grad"):null;return D.tidy(()=>{let{value:s,grads:i}=D.gradients(()=>e(a),[a],r);return r!=null&&ln(s.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),$c(i),i[0]})}}function LE(e){return F(Nr(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{F(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let a=Gu(t,"args","tf.grads","string_or_numeric"),r=n!=null?M(n,"dy","tf.grads"):null;return D.tidy(()=>{let{value:s,grads:i}=D.gradients(()=>e(...a),a,r);return r!=null&&ln(s.shape,r.shape,"The shape of dy passed in 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i=M(e,"boxes","nonMaxSuppressionAsync"),o=M(t,"scores","nonMaxSuppressionAsync"),l=Tl(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([i.data(),o.data()]),d=u[0],p=u[1],{selectedIndices:c,selectedScores:h}=k3(d,p,n,a,r,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:Ct(c,"int32"),selectedScores:Ct(h)}}var MM=RM;function FM(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=M(e,"boxes","nonMaxSuppression"),o=M(t,"scores","nonMaxSuppression"),l=Tl(i,o,n,a,r,null),u=l.maxOutputSize,d=l.iouThreshold,p=l.scoreThreshold,c={boxes:i,scores:o},h={maxOutputSize:u,iouThreshold:d,scoreThreshold:p,padToMaxOutputSize:s},m=D.runKernel(Po,c,h);return{selectedIndices:m[0],validOutputs:m[1]}}var $M=O({nonMaxSuppressionPadded_:FM});async function DM(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let 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_M(e,t,n=!1,a=!1){let r=M(e,"images","resizeNearestNeighbor");F(r.rank===3||r.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`),F(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),F(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),F(a===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=r,i=!1;r.rank===3&&(i=!0,s=H(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:a,size:t},u=D.runKernel($u,o,l);return i?H(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var N3=O({resizeNearestNeighbor_:_M});function PM(e,t="binary",n=!1,a=.5){let r=M(e,"image","threshold"),s=.2989,i=.587,o=.114,l=r.shape[0]*r.shape[1],u=P(Ct([a]),255),d,p,c,h;if(F(r.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${r.rank}.`),F(r.shape[2]===3||r.shape[2]===1,()=>`Error 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D.runKernel(tl,l,u)}var VM=O({transform_:BM});function jM(e,t,n){F(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),F(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let a=M(e,"a","bandPart");F(a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${a.rank}.`);let r=a.shape,[s,i]=a.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=H(Il(0,s,1,"int32"),[-1,1]),l=Il(0,i,1,"int32"),u=ge(o,l),d=pa(Lr(u,Se(+t,"int32")),Pr(u,Se(-n,"int32"))),p=Mt([s,i],a.dtype);return H(pn(ca(H(a,[-1,s,i])).map(c=>rn(d,c,p))),r)}var UM=O({bandPart_:jM});function HM(e){let t;if(Array.isArray(e)){t=!1,F(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths 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r=M(e,"labels","hingeLoss"),s=M(t,"predictions","hingeLoss"),i=null;n!=null&&(i=M(n,"weights","hingeLoss")),ln(r.shape,s.shape,"Error in hingeLoss: ");let o=Se(1);r=ge(P(Se(2),r),o);let l=Va(ge(o,P(r,s)));return dr(l,i,a)}var tF=O({hingeLoss_:eF});function nF(e,t,n,a=1,r=cn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","huberLoss"),i=M(t,"predictions","huberLoss"),o=null;n!=null&&(o=M(n,"weights","huberLoss")),ln(s.shape,i.shape,"Error in huberLoss: ");let l=Se(a),u=Lt(ge(i,s)),d=wl(u,l),p=ge(u,d),c=oe(P(Se(.5),it(d)),P(l,p));return dr(c,o,r)}var aF=O({huberLoss_:nF});function rF(e,t,n,a=1e-7,r=cn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","logLoss"),i=M(t,"predictions","logLoss"),o=null;n!=null&&(o=M(n,"weights","logLoss")),ln(s.shape,i.shape,"Error in logLoss: ");let l=Se(1),u=Se(a),d=wt(P(s,zn(oe(i,u)))),p=P(ge(l,s),zn(oe(ge(l,i),u))),c=ge(d,p);return dr(c,o,r)}var sF=O({logLoss_:rF});function iF(e,t,n,a=cn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","meanSquaredError"),s=M(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=M(n,"weights","meanSquaredError")),ln(r.shape,s.shape,"Error in meanSquaredError: ");let o=qc(r,s);return dr(o,i,a)}var oF=O({meanSquaredError_:iF});function lF(e,t){let n=M(e,"labels","sigmoidCrossEntropyWithLogits"),a=M(t,"logits","sigmoidCrossEntropyWithLogits");ln(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Va(a),s=P(a,n),i=Fc(Jn(wt(Lt(a))));return oe(ge(r,s),i)}function uF(e,t,n,a=0,r=cn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"multiClassLabels","sigmoidCrossEntropy"),i=M(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=M(n,"weights","sigmoidCrossEntropy")),ln(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let u=Se(a),d=Se(1),p=Se(.5);s=oe(P(s,ge(d,u)),P(p,u))}let l=lF(s,i);return dr(l,o,r)}var dF=O({sigmoidCrossEntropy_:uF});function pF(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet 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Labels / logits was rank ${t.rank} and dim was ${n}`);return Wa((a,r,s)=>{let i=NA(r,[n],!0),o=ge(me(r,"float32"),i);s([a,o]);let l=wt(P(o,a));return{value:ke(l,[n]),gradFunc:(u,d)=>{let[p,c]=d,h=gi(u.shape,[n]);return[P(H(u,h),ge(me(p,"float32"),Jn(c))),P(H(u,h),ge(Jn(c),me(p,"float32")))]}}})(e,t)}function cF(e,t,n,a=0,r=cn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"onehotLabels","softmaxCrossEntropy"),i=M(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=M(n,"weights","softmaxCrossEntropy")),ln(s.shape,i.shape,"Error in softmaxCrossEntropy: "),a>0){let u=Se(a),d=Se(1),p=Se(s.shape[1]);s=oe(P(s,ge(d,u)),Ae(u,p))}let l=pF(s,i);return dr(l,o,r)}var hF=O({softmaxCrossEntropy_:cF});function fF(e,t,n,a){let r=M(e,"indices","sparseFillEmptyRows"),s=M(t,"values","sparseFillEmptyRows"),i=M(n,"denseShape","sparseFillEmptyRows"),o=M(a,"defaultValue","sparseFillEmptyRows",s.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape ${r.shape}`);if(s.rank!==1)throw new 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this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Qb(e,t)}dispose(){this.iterations_!=null&&Ee(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Se(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(pr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var th=class extends pr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=D.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:W(()=>Ue(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:W(()=>Ue(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;W(()=>{let l=oe(P(i,this.rho),P(it(s),1-this.rho)),u=P(Ae(en(oe(o,this.epsilon)),en(oe(i,this.epsilon))),s),d=oe(P(o,this.rho),P(it(u),1-this.rho));i.assign(l),o.assign(d);let p=oe(P(u,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ee(this.accumulatedGrads.map(e=>e.variable)),Ee(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};th.className="Adadelta";Dr(th);var nh=class extends pr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=D.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:W(()=>xl(a.shape,this.initialAccumulatorValue).variable(i))}}let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;W(()=>{let i=oe(s,it(r));s.assign(i);let o=oe(P(Ae(r,en(oe(i,D.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ee(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};nh.className="Adagrad";Dr(nh);var ah=class extends pr{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],W(()=>{this.accBeta1=Se(t).variable(),this.accBeta2=Se(n).variable()}),a==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);W(()=>{let n=ge(1,this.accBeta1),a=ge(1,this.accBeta2);t.forEach((r,s)=>{let i=D.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:W(()=>Ue(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:W(()=>Ue(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,p=oe(P(u,this.beta1),P(l,1-this.beta1)),c=oe(P(d,this.beta2),P(it(l),1-this.beta2)),h=Ae(p,n),m=Ae(c,a);u.assign(p),d.assign(c);let f=oe(P(Ae(h,oe(en(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(P(this.accBeta1,this.beta1)),this.accBeta2.assign(P(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ee(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),W(()=>{this.accBeta1.assign(ur(this.beta1,this.iterations_+1)),this.accBeta2.assign(ur(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};ah.className="Adam";Dr(ah);var rh=class extends pr{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],W(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),a==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);W(()=>{let n=ge(1,this.accBeta1),a=Ae(-this.learningRate,oe(P(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=D.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ue(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Ue(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedWeightedInfNorm[s].variable,p=oe(P(u,this.beta1),P(l,1-this.beta1)),c=P(d,this.beta2),h=Lt(l),m=Ba(c,h);u.assign(p),d.assign(m);let f=oe(P(Ae(a,n),Ae(p,oe(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(oe(this.iteration,1)),this.accBeta1.assign(P(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ee(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};rh.className="Adamax";Dr(rh);var dd=class extends pr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=D.registeredVariables[t];W(()=>{let s=oe(P(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Gt(Se(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};sh.className="Momentum";Dr(sh);var ih=class extends pr{constructor(e,t=.9,n=0,a=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=D.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=D.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:W(()=>Ue(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:W(()=>Ue(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:W(()=>Ue(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;W(()=>{let l=oe(P(i,this.decay),P(it(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,d=oe(P(u,this.decay),P(s,1-this.decay)),p=Ae(P(s,this.learningRate),en(ge(l,oe(it(d),this.epsilon)))),c=oe(P(o,this.momentum),p);i.assign(l),u.assign(d),o.assign(c);let h=ge(a,c);a.assign(h)}else{let 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d=R.computePool3DInfo(s.shape,i,o,1,l,u),p=d.strideDepth,c=d.strideHeight,h=d.strideWidth,m=d.filterDepth,f=d.filterHeight,A=d.filterWidth,y=d.dilationDepth,g=d.dilationHeight,x=d.dilationWidth,w=d.effectiveFilterDepth,b=d.effectiveFilterHeight,v=d.effectiveFilterWidth,N=w-1-d.padInfo.front,T=v-1-d.padInfo.left,C=b-1-d.padInfo.top,$=We(s.shape,"float32"),z=1/(m*f*A),_=n.bufferSync(r);for(let V=0;V=d.outDepth||Math.floor(Q)!==Q))for(let ue=0;ue=d.outHeight||Math.floor(J)!==J))for(let re=0;re=d.outWidth||Math.floor(fe)!==fe||(ne+=_.get(V,Q,J,fe,U))}}}$.set(ne*z,V,j,X,G,U)}return n.makeTensorInfo($.shape,$.dtype,$.values)}var OD={kernelName:zp,backendName:"cpu",kernelFunc:zD};function _D(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ve([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,d=R.computePool2DInfo(i.shape,o,l,1,u),p=d.strideHeight,c=d.strideWidth,h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,A=d.dilationWidth,y=d.effectiveFilterHeight,g=d.effectiveFilterWidth,x=g-1-d.padInfo.left,w=y-1-d.padInfo.top,b=We(i.shape,"float32"),v=1/(h*m),N=n.data.get(r.dataId).values,T=We(r.shape,"float32",N);for(let C=0;C=d.outHeight||Math.floor(G)!==G))for(let te=0;te=d.outWidth||Math.floor(Y)!==Y||(j+=T.get(C,G,Y,$))}}b.set(j*v,C,z,_,$)}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var PD={kernelName:Dp,backendName:"cpu",kernelFunc:_D};function LD(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;k.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean 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o=s.reduce((y,g)=>y*g),l=R.getReshaped(r.shape,s,o),u=R.getPermuted(l.length,s.length),d=R.getReshapedPermuted(r.shape,s,o),p=R.getSliceBeginCoords(i,s.length),c=R.getSliceSize(d,i,s.length),h=ct({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ea({inputs:{x:h},backend:n,attrs:{perm:u}}),f=ct({inputs:{x:m},backend:n,attrs:{shape:d}}),A=ki({inputs:{x:f},backend:n,attrs:{begin:p,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),A}var VD={kernelName:ku,backendName:"cpu",kernelFunc:BD};function jD(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=KA(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var UD={kernelName:Op,backendName:"cpu",kernelFunc:jD},HD=at(Er,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new 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Pt(c.outShape,r.dtype),b=k.computeStrides(r.shape),v=k.computeStrides(s.shape),N=b[0],T=x?b[1]:b[2],C=x?b[2]:1,$=x?1:b[1],z=w.strides[0],_=x?w.strides[1]:w.strides[2],V=x?w.strides[2]:1,U=x?1:w.strides[1],j=n.data.get(r.dataId).values,X=n.data.get(s.dataId).values,G=w.values;for(let te=0;te=c.inHeight)continue;let re=ue*v[0],fe=Y+J*T;for(let Ie=0;Ie=c.inWidth)continue;let Qe=re+Oe*v[1],et=fe+De*C,st=Qe;for(let Ke=0;Ke=u.inDepth)continue;let te=X*C[0],Y=z+G*T[1];for(let ie=0;ie=u.inHeight)continue;let J=te+Q*C[1],re=Y+ue*T[2];for(let fe=0;fe=u.inWidth)continue;let De=J+$e*C[2],Qe=re+Oe*u.inChannels,et=De;for(let st=0;stMath.cos(e)),uz={kernelName:gs,backendName:"cpu",kernelFunc:lz},dz=at(mo,e=>Math.cosh(e)),pz={kernelName:mo,backendName:"cpu",kernelFunc:dz};function 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xe(t,()=>t.attachShader(a,this.vertexShader)),xe(t,()=>t.attachShader(a,n)),_7(t,a),this.debug&&mh(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=cv(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&xe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&mh(this.gl,this.program),xe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?U7(this.gl,e,t):H7(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),xe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return 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this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=yd(this.gl,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await 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e=yL(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&k.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Ah(this.gl,e,this.framebuffer),this.debug&&gd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Ah(this.gl,this.outputTexture,this.framebuffer),this.debug&&gd(this.gl)):d1(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;Ah(a,e,this.framebuffer),this.debug&&gd(a),this.outputTexture=e,xe(a,()=>a.viewport(0,0,t,n)),xe(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),xe(this.gl,()=>this.gl.scissor(e,t,n,a))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function yL(e){let t=0;for(;t{let m=k.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(` `),i=e.map(h=>xL(h,t,a)).join(` `),o=t.texShape,l=hn(),u=wL(l),d,p,c=SL(l);return t.isPacked?(d=bL(t.logicalShape,o),p=IL(l)):(d=vL(t.logicalShape,o),p=kL(l)),a&&(c+=CL),[c,u,p,s,d,i,n].join(` `)}function Dl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return BL(e);case 1:return jL(e);case 2:return 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Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:ta.UPLOAD,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a,r){if(ee().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. 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t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Pl(a,wh):h=new Vr(a,wh);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ee().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&ee().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...Ad(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];d=R.mergeRealAndImagArrays(m,f)}else if(l==null)d=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}u!=null&&this.disposeIntermediateTensorInfo(u);let p=this.convertAndCacheOnCPU(e,d),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ir().removeDataId(e,this),this.pendingDeletes--),p}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=aB){return ee().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&k.sizeFromShape(n.shape)0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return ir().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new JW(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new FW(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Si(e.shape),...Ni(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Si(t),...Ni(t)],s=new Tv(r,n),i=!0,o=this.runWebGLProgram(s,[a],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=yh(a),i;n?i=new cL(s):i=new pL(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,null,o);return{dtype:r,shape:a,dataId:l.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===fd.DENSE){let f=Ad(e.outputShape);i.texShape=f.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(s.shape)===0)return i.values=k.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(f.dataId);if(A.texture==null){if(!e.packedInputs&&k.sizeFromShape(f.shape)<=ee().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=f.shape)}else if(!!A.isPacked!=!!e.packedInputs)f=A.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),A=this.texData.get(f.dataId);else if(A.isPacked&&!xd(A.shape,f.shape)){let y=f,g=f.shape;f.shape=A.shape,f=this.packedReshape(f,g),o.push(f),A=this.texData.get(f.dataId),y.shape=g}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},d=nW(e,l,u),p=this.getAndSaveBinary(d,()=>eW(this.gpgpu,e,l,u)),c=this.activeTimers!=null,h;c&&(h=this.startTimer()),tW(this.gpgpu,p,l,u,a),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),c&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let m=ee().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=k.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!ee().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(ee().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=W(()=>{if(!ee().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=ee().getBool("DEBUG");ee().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(ee().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?eB:tB}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=k.now());let d=t.texShape;if(d==null&&(d=K7(n,o),t.texShape=d),r!=null){let p=yh(n),c,h=d[1],m=d[0],f=r instanceof Uint8Array;o?([h,m]=Fl(d[0],d[1]),c=new AL(p,[m,h],f)):c=new mL(p,[m,h],f);let A=this.makeTensorInfo([m,h],a);f?this.texData.get(A.dataId).usage=ta.PIXELS:this.texData.get(A.dataId).usage=ta.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),h,m,r);let y=!0,g=this.runWebGLProgram(c,[A],a,null,y),x=this.texData.get(g.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=k.now()-u)}else{let p=this.acquireTexture(d,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=iB(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};Ll.nextDataId=0;function iB(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;anew Ll,2);var oB={forceHalfFloat:$v},Dv=` if (isnan(a)) return a; if (isnan(b)) return b; `,Wl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=` float binaryOperation(float a, float b) { ${e} } void main() { float a = getAAtOutCoords(); float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } `}},kh=` result.r = isNaN.r > 0. ? NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; `,vd=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=R.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(s=` ${lt(r)} coords = getOutputCoords(); `,r===1)s+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let i=fn("coords",r);s+=` bool nextRowOutOfBounds = (${i[r-2]} + 1) >= ${this.outputShape[r-2]}; bool nextColOutOfBounds = (${i[r-1]} + 1) >= ${this.outputShape[r-1]}; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; `}this.userCode=` vec4 binaryOperation(vec4 a, vec4 b) { ${e} } void main() { vec4 a = getAAtOutCoords(); vec4 b = getBAtOutCoords(); vec4 result = binaryOperation(a, b); ${s} setOutput(result); } `}};function Wn(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var lB={kernelName:Ts,backendName:"webgl",kernelFunc:Wn};function jr(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=Wn({inputs:{x:a},backend:n}),l=Wn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var uB={kernelName:_p,backendName:"webgl",kernelFunc:jr},zv="return (a < 0.) ? b * a : a;",Ov=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function dB(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vd(Ov,r.shape,i.shape):new Wl(zv,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var pB={kernelName:Es,backendName:"webgl",kernelFunc:dB},_v="return (a < 0.) ? b * a : a;",Pv=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function cB(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vd(Pv,a.shape,r.shape):new Wl(_v,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var hB={kernelName:Bs,backendName:"webgl",kernelFunc:cB},Lv="if (isnan(x)) return x;",fB=` if (isnan(a)) return a; if (isnan(b)) return b; `,mB=` result.r = isNaN.r > 0. ? NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; `;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),c=n(p.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new Pl(i.shape,t):d=new Vr(i.shape,e),o.runWebGLProgram(d,[i],l)}}function nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(a&&l.dtype==="complex64"){let m=d.texData.get(l.dataId),f=d.texData.get(u.dataId),[A,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[w,b]=x,v={dataId:w.dataId,dtype:w.dtype,shape:l.shape},N={dataId:b.dataId,dtype:b.dtype,shape:u.shape},T=new Wl(e,l.shape,u.shape);return d.runWebGLProgram(T,[v,N],ua(w.dtype,b.dtype))}),g=jr({inputs:{real:A,imag:y},backend:d});return d.disposeIntermediateTensorInfo(A),d.disposeIntermediateTensorInfo(y),g}let p=s||ua(l.dtype,u.dtype);if(d.shouldExecuteOnCPU([l,u])&&r!=null){let m=d.texData.get(l.dataId),f=d.texData.get(u.dataId),[A,y]=r(l.shape,u.shape,m.values,f.values,p),g=d.makeTensorInfo(y,p),x=d.texData.get(g.dataId);return x.values=A,g}let c=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new vd(t,l.shape,u.shape,n):h=new Wl(e,l.shape,u.shape),d.runWebGLProgram(h,[l,u],p)}}function Ih(e,t=!1){if(e==="linear")return t?qW:VW;if(e==="relu")return t?KW:UW;if(e==="elu")return t?XW:jW;if(e==="relu6")return t?ZW:HW;if(e==="prelu")return t?Pv:_v;if(e==="leakyrelu")return t?Ov:zv;if(e==="sigmoid")return t?YW:GW;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Wv=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=a?e[1]:e[2],d=Math.ceil(u/2),p=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",A="";i&&(o?f=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?f=`vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); ${i} }`:f=`vec4 activation(vec4 x) { ${i} }`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",x="rc.x";e[0]`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. 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} return asin(x); `,QB=Xe({opSnippet:JB}),eV={kernelName:lo,backendName:"webgl",kernelFunc:QB},tV=wa+"return log(x + sqrt(x * x + 1.0));",nV=Xe({opSnippet:tV}),aV={kernelName:uo,backendName:"webgl",kernelFunc:nV},rV=wa+` return atan(x); `,sV=Xe({opSnippet:rV}),iV={kernelName:po,backendName:"webgl",kernelFunc:sV},oV=fB+` return atan(a, b); `,lV=` vec4 result = atan(a, b); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); `+mB+` return result; `,uV=nn({opSnippet:oV,packedOpSnippet:lV}),dV={kernelName:ho,backendName:"webgl",kernelFunc:uV},pV=wa+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,cV=Xe({opSnippet:pV}),hV={kernelName:co,backendName:"webgl",kernelFunc:cV},wd=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${c}, ${h}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; float avgValue = 0.0; for (int wR = 0; wR < ${d}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${p}; wC += ${u}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float value = getX(batch, xR, xC, d); // If a min / max value has already been found, use it. If not, // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); if (value ${N} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${a?r?f:A:`wR * ${p} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let g="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let w=Math.floor(s/4)*4,b=s%4,v=` if (${m}) { avgValue += dot(values, ones); } else { minMaxValue = ${g}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${c}, ${h}); const float initializationValue = ${y}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xR, int xC, int d) { if (xC < 0 || xC >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${y}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${d}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${w}; wC += 4) { int xC = xCCorner + wC * ${u}; vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), getValue(batch, xR, xC + 2 * ${u}, d), getValue(batch, xR, xC + 3 * ${u}, d) ); ${v} } int xC = xCCorner + ${w}; if (${b===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${v} } else if (${b===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), initializationValue, initializationValue ); ${v} } else if (${b===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), getValue(batch, xR, xC + 2 * ${u}, d), initializationValue ); ${v} } } setOutput(${x}); } `}},w1=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,p=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",x="0.0";if(g||(x="-1.0 / 1e-20"),n){let C=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${A}, ${y}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; for (int wD = 0; wD < ${c}; wD += ${u}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR += ${d}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${m}; wC += ${p}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float value = getX(batch, xD, xR, xC, ch); // If a min / max value has already been found, use it. If not, // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); if (value ${C} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} + wR * ${m} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let w="max",b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(b="avgValue / count");let v=Math.floor(s/4)*4,N=s%4,T=` if (${g}) { avgValue += dot(values, ones); } else { minMaxValue = ${w}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${A}, ${y}); const float initializationValue = ${x}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xD, int xR, int xC, int ch) { if (xC < 0 || xC >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${x}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${c}; wD += ${u}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR += ${d}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${v}; wC += 4) { int xC = xCCorner + wC * ${p}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${p}, ch), getValue(batch, xD, xR, xC + 2 * ${p}, ch), getValue(batch, xD, xR, xC + 3 * ${p}, ch) ); ${T} } int xC = xCCorner + ${v}; if (${N===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${T} } else if (${N===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${p}, ch), initializationValue, initializationValue ); ${T} } else if (${N===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${p}, ch), getValue(batch, xD, xR, xC + 2 * ${p}, ch), initializationValue ); ${T} } } setOutput(${b}); } } `}};function fV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;$l(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=R.computePool2DInfo(r.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return Wn({inputs:{x:r},backend:n});let p=new wd(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var mV={kernelName:cs,backendName:"webgl",kernelFunc:fV};function AV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,d=[1,1,1],p=R.computePool3DInfo(r.shape,s,i,d,o,l,u),c=new w1(p,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var yV={kernelName:wu,backendName:"webgl",kernelFunc:AV},gV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,p=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${d}); const float avgMultiplier = float(${p}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${o}; wR += ${s}) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${l}; wC+= ${i}) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},xV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=d-1-e.padInfo.front,m=p-1-e.padInfo.top,f=c-1-e.padInfo.left,A=1/(t*n*a);this.userCode=` const ivec3 pads = ivec3(${h}, ${m}, ${f}); const float avgMultiplier = float(${A}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${d}; wD += ${o}) { float dyD = float(dyDCorner + wD) / ${r}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${p}; wR += ${l}) { float dyR = float(dyRCorner + wR) / ${s}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${c}; wC += ${u}) { float dyC = float(dyCCorner + wC) / ${i}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); dotProd += dyValue * avgMultiplier; } } } setOutput(dotProd); } `}};function bV(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=a,p=[1,1,1],c=R.computePool3DInfo(i.shape,o,l,p,u,d),h=new xV(c);return n.runWebGLProgram(h,[r],i.dtype)}var vV={kernelName:zp,backendName:"webgl",kernelFunc:bV};function wV(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;$l([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,d=R.computePool2DInfo(i.shape,o,l,1,u),p=new gV(d);return n.runWebGLProgram(p,[r],i.dtype)}var kV={kernelName:Dp,backendName:"webgl",kernelFunc:wV};function IV(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Th({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var SV={kernelName:hs,backendName:"webgl",kernelFunc:IV},NV=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); float offset = ${i}; float scale = ${o}; float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } `}},TV=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); vec4 inv = scale * inversesqrt(variance + vec4(${s})); setOutput((x - mean) * inv + offset); } `}},EV=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],d=null;i!=null&&(d=i.shape,u.push(i));let p=null;o!=null&&(p=o.shape,u.push(o));let c=ee().getBool("WEBGL_PACK_NORMALIZATION")?new TV(a.shape,r.shape,s.shape,d,p,l):new NV(a.shape,r.shape,s.shape,d,p,l);return t.runWebGLProgram(c,u,u[0].dtype)},CV={kernelName:Ss,backendName:"webgl",kernelFunc:EV},RV=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=`uniform int start[${this.rank}];`,a=MV(this.rank),r,s=e.map((i,o)=>`sourceLoc.${k1[o]} = start[${o}] + coords.${k1[o]};`);r=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${s.join(` `)} `,this.userCode=` ${n} void main() { ${r} setOutput(getSource(${a})); } `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},k1=["x","y","z","w","u","v"];function MV(e){if(e===1)return"sourceLoc";if(e<=6)return k1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var FV=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=fn("coords",this.rank),a=fn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=` result.x = ${s}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${a[this.rank-1]}; 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${n} }`:r?x=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:x=` float activation(float x) { ${n} } `,w="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${x} const ivec2 strides = ivec2(${o}, ${l}); const ivec2 pads = ivec2(${s}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${g}]; ivec2 xRCCorner = ivec2(coords[${A}], coords[${y}]) * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${p}; wR++) { int xR = xRCorner + wR * ${u}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${c}; wC++) { int xC = xCCorner + wC * ${d}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${h}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${f}) { vec4 xValues = vec4( getX(batch, xR, xC, d1), getX(batch, xR, xC, d1 + 1), getX(batch, xR, xC, d1 + 2), getX(batch, xR, xC, d1 + 3) ); dotProd += dot(xValues, wValues); } else { vec4 xValues = vec4( getX(batch, d1, xR, xC), getX(batch, d1 + 1, xR, xC), getX(batch, d1 + 2, xR, xC), getX(batch, d1 + 3, xR, xC) ); dotProd += dot(xValues, wValues); } } if (${m===1}) { if (${f}) { dotProd += getX(batch, xR, xC, ${h}) * getW(wR, wC, ${h}, d2); } else { dotProd += getX(batch, ${h}, xR, xC) * getW(wR, wC, ${h}, d2); } } else if (${m===2}) { vec2 wValues = vec2( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2) ); if (${f}) { vec2 xValues = vec2( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( getX(batch, ${h}, xR, xC), getX(batch, ${h} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${m===3}) { vec3 wValues = vec3( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2), getW(wR, wC, ${h} + 2, d2) ); if (${f}) { vec3 xValues = vec3( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1), getX(batch, xR, xC, ${h} + 2) ); dotProd += dot(xValues, wValues); } else { vec3 xValues = vec3( getX(batch, ${h}, xR, xC), getX(batch, ${h} + 1, xR, xC), getX(batch, ${h} + 2, xR, xC) ); dotProd += dot(xValues, wValues); } } } } float result = dotProd; ${b} ${w} setOutput(result); } `}},sj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${n}, ${a}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d2 = coords.u; ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xFCorner = xFRCCorner.x; int xRCorner = xFRCCorner.y; int xCCorner = xFRCCorner.z; // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get // y(yF, yR, yC, d2). ? = to be determined. : = across all // values in that axis. float dotProd = 0.0; for (int wF = 0; wF < ${d}; wF++) { int xF = xFCorner + wF * ${o}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${p}; wR++) { int xR = xRCorner + wR * ${l}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${c}; wC++) { int xC = xCCorner + wC * ${u}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${h}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${m===1}) { dotProd += getX(batch, xF, xR, xC, ${h}) * getW(wF, wR, wC, ${h}, d2); } else if (${m===2}) { vec2 xValues = vec2( getX(batch, xF, xR, xC, ${h}), getX(batch, xF, xR, xC, ${h} + 1) ); vec2 wValues = vec2( getW(wF, wR, wC, ${h}, d2), getW(wF, wR, wC, ${h} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${m===3}) { vec3 xValues = vec3( getX(batch, xF, xR, xC, ${h}), getX(batch, xF, xR, xC, ${h} + 1), getX(batch, xF, xR, xC, ${h} + 2) ); vec3 wValues = vec3( getW(wF, wR, wC, ${h}, d2), getW(wF, wR, wC, ${h} + 1, d2), getW(wF, wR, wC, ${h} + 2, d2) ); dotProd += dot(xValues, wValues); } } } } setOutput(dotProd); } `}},ij=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:d,dataFormat:p}=n,{left:c,top:h}=o,m=r*a,f=hn(),A=p==="channelsLast",y=A?0:1,g=A?1:2,x="";for(let w=0;w<=1;w++)for(let b=0;b<=1;b++)x+=` blockIndex = rc.y + ${b}; pos = rc.x + ${w}; if(blockIndex < ${e[1]} && pos < ${e[0]}) { offsetY = int(blockIndex / (${l})) * ${i} - ${h}; d0 = offsetY + ${d} * (pos / ${m}); if(d0 < ${t[y]} && d0 >= 0) { offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${c}.); d1 = offsetX + ${u} * (int(mod(float(pos), ${m}.) / ${r}.)); if(d1 < ${t[g]} && d1 >= 0) { ch = int(mod(float(pos), ${r}.)); if (${A}) { innerDims = vec2(d1, ch); result[${w*2+b}] = getChannel( getA(d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${w*2+b}] = getChannel( getA(ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } `;this.userCode=` void main() { ivec2 rc = getOutputCoords(); vec4 result = vec4(0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${x} ${f.output} = result; } `}};function nw({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),d=n.inChannels,p=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,A,y=[],g=(p===1||c===1)&&d>Hv,x=l[2]%2!=0&&!!u.isPacked;if(g||!ee().getBool("WEBGL_LAZILY_UNPACK")||!ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let w=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],b=ye({inputs:{x:e},backend:a,attrs:{shape:[1,w,n.inChannels]}}),v=ye({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Th({a:b,b:v,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=ye({inputs:{x:N},backend:a,attrs:{shape:n.outShape}}),y.push(b),y.push(v),y.push(N)}else{let w=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),b={dataId:e.dataId,shape:[1,w,n.inChannels],dtype:e.dtype},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,k.assert(xd(u.shape,b.shape),()=>`packed reshape ${u.shape} to ${b.shape} isn't free`);let N=ye({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=Th({a:b,b:N,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(T.dataId);k.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,C.shape=n.outShape,A=Wn({inputs:{x:T},backend:a}),A.shape=n.outShape,y.push(T)}for(let w of y)a.disposeIntermediateTensorInfo(w);return A}function aw({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:p,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*d,A=c*p,y=[f,A],g=!0,x=!1,w=[],b=ye({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),v=ye({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});w.push(b),w.push(v);let N=new ij(y,b.shape,n),T=a.runWebGLProgram(N,[b],"float32"),C=ye({inputs:{x:T},backend:a,attrs:{shape:[1,y[0],y[1]]}});w.push(T),w.push(C);let $=r!=null,z=s!=null,_=o==="leakyrelu",V=o?Ih(o,!0):null,U=new Wv(C.shape,v.shape,[1,A,n.outChannels],g,x,$,V,z,_),j=[C,v];if(r&&j.push(r),z&&j.push(s),_){let Y=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));j.push(Y),w.push(Y)}let X=a.runWebGLProgram(U,j,"float32"),G=m?[1,c,p,n.outChannels]:[1,n.outChannels,c,p],te=ye({inputs:{x:X},backend:a,attrs:{shape:G}});w.push(X);for(let Y of w)a.disposeIntermediateTensorInfo(Y);return te}function oj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=a,p=R.convertConv2DDataFormat(l),c=R.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!1,p),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=nw({x:r,filter:s,convInfo:c,backend:n});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=aw({x:r,filter:s,convInfo:c,backend:n});else{let f=new tw(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ye({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var lj={kernelName:As,backendName:"webgl",kernelFunc:oj},uj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int d2 = coords.w; // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${t} - ${a}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${r}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } if (${s}) { float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } else { float dyValue = getDy(b, d2, yR, yC); float xValue = getX(b, d1, xR, xC); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},dj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,d=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${d}]; ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${t}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { if (${s}) { float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } else { float xValue = getDy(batch, d2, idyR, idyC); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}},pj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; int wR = coords.y; int wC = coords.z; int d1 = coords.w; int d2 = coords.u; float dotProd = 0.0; for (int b = 0; b < ${e.batchSize}; b++) { for (int yF = 0; yF < ${e.outDepth}; yF++) { int xF = wF + yF * ${t} - ${r}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${n} - ${s}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${a} - ${i}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},cj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${u}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyFCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; float dotProd = 0.0; for (int wF = 0; wF < ${t}; wF++) { float dyF = float(dyFCorner + wF) / ${r}.0; if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) { continue; } int idyF = int(dyF); int wFPerm = ${t} - 1 - wF; for (int wR = 0; wR < ${n}; wR++) { float dyR = float(dyRCorner + wR) / ${s}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${n} - 1 - wR; for (int wC = 0; wC < ${a}; wC++) { float dyC = float(dyCCorner + wC) / ${i}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${a} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function hj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=a,p=R.convertConv2DDataFormat(l),c=R.computeConv2DInfo(r.shape,d,i,1,o,u,!1,p),h=new uj(c);return n.runWebGLProgram(h,[r,s],"float32")}var fj={kernelName:Pp,backendName:"webgl",kernelFunc:hj};function mj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=a,p=R.convertConv2DDataFormat(u),c=R.computeConv2DInfo(i,s.shape,o,1,l,d,!1,p),h=new dj(c);return n.runWebGLProgram(h,[r,s],"float32")}var Aj={kernelName:ys,backendName:"webgl",kernelFunc:mj};function yj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=R.computeConv3DInfo(r.shape,s.shape,i,l,o),d=new sj(u);return n.runWebGLProgram(d,[r,s],"float32")}var gj={kernelName:Su,backendName:"webgl",kernelFunc:yj};function xj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=R.computeConv3DInfo(r.shape,l,i,1,o),d=new pj(u);return n.runWebGLProgram(d,[r,s],"float32")}var bj={kernelName:Lp,backendName:"webgl",kernelFunc:xj};function vj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=R.computeConv3DInfo(l,s.shape,o,1,i),d=new cj(u);return n.runWebGLProgram(d,[r,s],"float32")}var wj={kernelName:Wp,backendName:"webgl",kernelFunc:vj},kj=Lv+` return cos(x); `,Ij=Xe({opSnippet:kj}),Sj={kernelName:gs,backendName:"webgl",kernelFunc:Ij},Nj=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,Tj=Xe({opSnippet:Nj}),Ej={kernelName:mo,backendName:"webgl",kernelFunc:Tj},Cj=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,p]=n;this.outputShape=[u,d,p,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,A,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[g,x,w]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` const float height_ratio = float(${f}); const float width_ratio = float(${g}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int y = coords[1]; int x = coords[2]; int d = coords[3]; // get box vals float y1 = getBoxes(b,0); float x1 = getBoxes(b,1); float y2 = getBoxes(b,2); float x2 = getBoxes(b,3); // get image in batch index int bInd = round(getBoxInd(b)); if(bInd < 0 || bInd >= ${s}) { return; } float height_scale = ${A}; float width_scale = ${x}; float in_y = ${y}; if( in_y < 0.0 || in_y > ${h} ) { setOutput(float(${r})); return; } float in_x = ${w}; if( in_x < 0.0 || in_x > ${m} ) { setOutput(float(${r})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${c} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}},Rj=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,d=new Cj(r.shape,s.shape,o,l,u);return n.runWebGLProgram(d,[r,s,i],"float32")},Mj={kernelName:Ao,backendName:"webgl",kernelFunc:Rj},rw=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${sw(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=` uniform float index; void main() { ${lt(a)} coords = getOutputCoords(); int end = ${iw(a,"coords")}; float val = ${r}; int pow2 = int(pow(2.0, index)); if (${i}) { int idx = ${o}; ${iw(a,"coords")} = idx; val += getX(${sw(a,"coords")}); } setOutput(val); } `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function sw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function iw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Fj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,u=R.getAxesPermutation([s],l),d=r;u!=null&&(d=mn({inputs:{x:r},backend:n,attrs:{perm:u}}));let p=R.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=d.shape[p],h=Wn({inputs:{x:d},backend:n});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new rw(d.shape,!1,o),A=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let m=new rw(d.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=R.getUndoAxesPermutation(u),f=mn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),f}return h}var $j={kernelName:xs,backendName:"webgl",kernelFunc:Fj};function Dj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),d=Iv(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),d=rW(l,u,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var zj={kernelName:Bp,backendName:"webgl",kernelFunc:Dj},Oj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int h = ${this.getHeightCoordString()}; int w = ${this.getWidthCoordString()}; int d = ${this.getDepthCoordString()}; int in_h = h / ${t}; int offset_h = imod(h, ${t}); int in_w = w / ${t}; int offset_w = imod(w, ${t}); int offset_d = (offset_h * ${t} + offset_w) * ${this.getOutputDepthSize()}; int in_d = d + offset_d; float result = ${this.getInputSamplingString()}; setOutput(result); } `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function _j(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,c=u*s,h=d/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=new Oj(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var Pj={kernelName:yo,backendName:"webgl",kernelFunc:_j},ow=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,d=e.strideWidth,p=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,A="",y="";n&&(a?A=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?A=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:A=` float activation(float x) { ${n} } `,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${A} const ivec2 strides = ivec2(${u}, ${d}); const ivec2 pads = ivec2(${o}, ${l}); void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${f}; int q = d2 - d1 * ${f}; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations. for (int wR = 0; wR < ${h}; wR++) { int xR = xRCorner + wR * ${p}; if (xR < 0 || xR >= ${s}) { continue; } for (int wC = 0; wC < ${m}; wC++) { int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${i}) { continue; } float xVal = getX(batch, xR, xC, d1); float wVal = getW(wR, wC, d1, q); dotProd += xVal * wVal; } } float result = dotProd; ${g} ${y} setOutput(result); } `}},lw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,d=e.strideHeight,p=e.strideWidth,c=e.dilationHeight,h=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,A=f,y=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let b=0;b=0 && xR < ${i}) { `;for(let v=0;v<(A+1)/2;v++){let N=v*2,T=N*h;if(y+=` xC = xCCorner + ${T}; `,p===1){if(N= 0 && xCOffset < ${o} && xTexelC${T}Ready == 0) { xTexelC${T} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= ${o}) { xTexelC${T}.zw = vec2(0.0); } xTexelC${T}Ready = 1; } `,h===1&&T>0?y+=` xC${N} = vec4(xTexelC${T-2}.zw, xTexelC${T}.xy); `:y+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < ${o}) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= ${o}) { previous.zw = vec2(0.0); } xC${N} = vec4(previous.zw, xTexelC${T}.xy); } else { xC${N} = vec4(0.0, 0.0, xTexelC${T}.xy); } `):y+=` if (xC >= 0 && xC < ${o} && xTexelC${T}Ready == 0) { xTexelC${T} = getX(batch, xR, xC, d1); if (xC + 1 >= ${o}) { xTexelC${T}.zw = vec2(0.0); } xTexelC${T}Ready = 1; } xC${N} = xTexelC${T}; `,T+1= 0 && xCOffset < ${o} && xTexelC${T+2}Ready == 0) { xTexelC${T+2} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= ${o}) { xTexelC${T+2}.zw = vec2(0.0); } xTexelC${T+2}Ready = 1; } `,h>1&&(y+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${T}Ready == 0) { xTexelC${T} = getX(batch, xR, xCOffset, d1); xTexelC${T}Ready = 1; } `),y+=` xC${N+1} = vec4(xTexelC${T}.zw, xTexelC${T+2}.xy); `):C===1?y+=` xC${N+1} = xTexelC${T}; `:y+=` xCOffset = xC + ${C}; if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${T+2}Ready == 0) { xTexelC${T+2} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= ${o}) { xTexelC${T+2}.zw = vec2(0.0); } xTexelC${T+2}Ready = 1; } xC${N+1} = xTexelC${T+2}; `}}else T= 0 && xCOffset < ${o} && xTexelC${T}Ready == 0) { xTexelC${T} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= ${o}) { xTexelC${T}.zw = vec2(0.0); } xTexelC${T}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < ${o} && xTexelC${T+2}Ready == 0) { xTexelC${T+2} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= ${o}) { xTexelC${T+2}.zw = vec2(0.0); } xTexelC${T+2}Ready = 1; } xC${N} = vec4(xTexelC${T}.zw, xTexelC${T+2}.zw); `,T+1= 0 && xCOffset < ${o}) { final = getX(batch, xR, xCOffset, d1); } xC${N+1} = vec4(xTexelC${T+2}.xy, final.xy); `)):(y+=` if(xC >= 0 && xC < ${o} && xTexelC${T}Ready == 0) { xTexelC${T} = getX(batch, xR, xC, d1); if (xC + 1 >= ${o}) { xTexelC${T}.zw = vec2(0.0); } xTexelC${T}Ready = 1; } xCOffset = xC + ${p}; if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${T+2}Ready == 0) { xTexelC${T+2} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= ${o}) { xTexelC${T+2}.zw = vec2(0.); } xTexelC${T+2}Ready = 1; } xC${N} = vec4( xTexelC${T}.xy, xTexelC${T+2}.xy); `,T+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=R.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),c;return ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?c=new lw(p):c=new ow(p),n.runWebGLProgram(c,[r,s],"float32")}var Wj={kernelName:bs,backendName:"webgl",kernelFunc:Lj},Bj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int dm = coords.w; int d2 = d1 * ${s} + dm; float dotProd = 0.0; // TO DO: Vec4 over the batch size for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${t} - ${a}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${r}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},Vj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${s}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[3]; ivec2 dyCorner = coords.yz - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; float dotProd = 0.0; for (int wR = 0; wR < ${t}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; // TO DO: Vec4 over the channelMul for (int dm = 0; dm < ${o}; dm++) { int d2 = d1 * ${o} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function jj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=a,p=R.computeConv2DInfo(r.shape,d,i,o,l,u,!0),c=new Bj(p);return n.runWebGLProgram(c,[r,s],"float32")}var Uj={kernelName:Vp,backendName:"webgl",kernelFunc:jj};function Hj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=a,p=R.computeConv2DInfo(d,s.shape,i,o,l,u,!0),c=new Vj(p);return n.runWebGLProgram(c,[r,s],"float32")}var Gj={kernelName:jp,backendName:"webgl",kernelFunc:Hj},qj=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function Xj(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=ye({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new qj(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ye({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var Kj={kernelName:Up,backendName:"webgl",kernelFunc:Xj},Zj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:p}=a;this.userCode=` const ivec2 strides = ivec2(${r}, ${s}); const ivec2 pads = ivec2(${d}, ${p}); const float neg_infinity = -3.4e38; void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.w; ivec2 outTopLeftCorner = coords.yz * strides - pads; int hBeg = outTopLeftCorner.x; int wBeg = outTopLeftCorner.y; float curVal = neg_infinity; for (int h = 0; h < ${i}; h++) { int hIn = hBeg + h * ${l}; if (hIn >= 0 && hIn < ${t}) { for (int w = 0; w < ${o}; w++) { int wIn = wBeg + w * ${u}; if (wIn >= 0 && wIn < ${n}) { float xVal = getX(batch, hIn, wIn, d1); float wVal = getW(h, w, d1); float val = xVal + wVal; if (val > curVal) { curVal = val; } } } } } float result = curVal; setOutput(result); } `}};function Yj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=R.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),d,p=new Zj(u);d=n.runWebGLProgram(p,[r,s],"float32");let c=ye({inputs:{x:d},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(d),c}var Jj={kernelName:Nu,backendName:"webgl",kernelFunc:Yj};function Qj(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=R.decodeEinsumEquation(r,s.length);R.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=R.getEinsumComputePath(o,l),p=d.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=Nh({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var eU={kernelName:qp,backendName:"webgl",kernelFunc:Qj},tU="return (x >= 0.0) ? x : (exp(x) - 1.0);",nU=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0); result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0); result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; `,aU=Xe({opSnippet:tU,packedOpSnippet:nU}),rU={kernelName:go,backendName:"webgl",kernelFunc:aU},sU="return (b >= 1.0) ? a : a * (b + 1.0);",iU=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,oU=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vd(iU,a.shape,r.shape):new Wl(sU,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},lU={kernelName:Xp,backendName:"webgl",kernelFunc:oU},uU=` return vec4(equal(a, b)); `,dU="return float(a == b);",pU=nn({opSnippet:dU,packedOpSnippet:uU,dtype:"bool"}),cU={kernelName:bo,backendName:"webgl",kernelFunc:pU},hU=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${R.ERF_P}; 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float unaryOpComplex(float real, float expR, float imag, float expI) { ${i} } float mulMatDFT(int batch, int index) { float indexRatio = float(index) / float(${a}); float exponentMultiplierTimesIndexRatio = exponentMultiplier * indexRatio; float result = 0.0; for (int i = 0; i < ${a}; i++) { // x = (-2|2 * PI / N) * index * i; float x = exponentMultiplierTimesIndexRatio * float(i); float expR = cos(x); float expI = sin(x); float real = getReal(batch, i); float imag = getImag(batch, i); result += unaryOpComplex(real, expR, imag, expI) / ${s}; } return result; } void main() { ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } `}};function hw(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ye({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new cw("real",l,t),d=new cw("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=jr({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=ye({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function bU(e){let{inputs:t,backend:n}=e,{input:a}=t;return hw(a,!1,n)}var vU={kernelName:Kp,backendName:"webgl",kernelFunc:bU},wU=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=` uniform float value; void main() { // Input can be obtained from uniform value. setOutput(value); } `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function N1(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new wU(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var kU={kernelName:Tu,backendName:"webgl",kernelFunc:N1},IU=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; int coordX = ${t} - x; float outputValue; if(coordX >= 0 && coordX < ${t}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } `}},SU={kernelName:ko,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new IU(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},fw="return floor(x);",NU=Xe({opSnippet:fw,packedOpSnippet:fw,cpuKernelImpl:uW}),TU={kernelName:ks,backendName:"webgl",kernelFunc:NU},EU=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); if (ib != 0) { // Windows (D3D) wants guaranteed non-zero int division at compile-time. return float(idiv(ia, ib, s)); } else { return NAN; } `,CU=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); ivec4 result = ivec4(0); vec4 s = sign(a) * sign(b); // Windows (D3D) wants guaranteed non-zero int division at compile-time. if (cond[0]) { result[0] = idiv(ia[0], ib[0], s[0]); } if (cond[1]) { result[1] = idiv(ia[1], ib[1], s[1]); } if (cond[2]) { result[2] = idiv(ia[2], ib[2], s[2]); } if (cond[3]) { result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); `,RU=nn({opSnippet:EU,packedOpSnippet:CU,dtype:"int32"}),MU={kernelName:Is,backendName:"webgl",kernelFunc:RU},FU=class{constructor(e){this.variableNames=["A"];let t=hn(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}},$U=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hn(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } result[row * 2 + col] = floor(value * 255.0 + 0.5); } } ${t.output} = result; } `}},DU={kernelName:pc,backendName:"webgl",kernelFunc:zU},Vl;function zU(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[u,l],p=[u,l,s];(o||i)&&(Vl==null&&(Vl=document.createElement("canvas").getContext("2d")),Vl.canvas.width=l,Vl.canvas.height=u,Vl.drawImage(r,0,0,l,u),r=Vl.canvas);let c=n.makeTensorInfo(d,"int32");n.texData.get(c.dataId).usage=ta.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=ee().getBool("WEBGL_PACK")?new $U(p):new FU(p),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function OU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=R.convertConv2DDataFormat(d),A=R.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!1,f),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=nw({x:r,filter:s,convInfo:A,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=aw({x:r,filter:s,convInfo:A,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let w=i!=null,b=o!=null,v=h==="leakyrelu",N=h?Ih(h,!1):null,T=new tw(A,w,N,b,v),C=[r,s];if(i&&C.push(i),o&&C.push(o),v){let $=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));C.push($),g.push($)}y=n.runWebGLProgram(T,C,"float32")}let x=ye({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}var _U={kernelName:si,backendName:"webgl",kernelFunc:OU};function PU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:p,activation:c,leakyreluAlpha:h}=a,m=[],f=d;f==null&&(f=[1,1]),k.assert(R.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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NAN : result.r; result.g = isNaN.g == 1.0 ? NAN : result.g; result.b = isNaN.b == 1.0 ? NAN : result.b; result.a = isNaN.a == 1.0 ? NAN : result.a; return result; `,IH=Xe({opSnippet:wH,packedOpSnippet:kH,cpuKernelImpl:fW}),SH={kernelName:Cs,backendName:"webgl",kernelFunc:IH},NH="return log(1.0 + x);",TH=Xe({opSnippet:NH}),EH={kernelName:Fo,backendName:"webgl",kernelFunc:TH},CH="return float(a >= 1.0 && b >= 1.0);",RH=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,MH=nn({opSnippet:CH,packedOpSnippet:RH,dtype:"bool"}),FH={kernelName:$o,backendName:"webgl",kernelFunc:MH},$H="return float(!(x >= 1.0));",DH=Xe({opSnippet:$H}),zH={kernelName:Eu,backendName:"webgl",kernelFunc:DH},OH="return float(a >= 1.0 || b >= 1.0);",_H=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,PH=nn({opSnippet:OH,packedOpSnippet:_H,dtype:"bool"}),LH={kernelName:Cu,backendName:"webgl",kernelFunc:PH},WH=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; for (int j = -${s}; j <= ${s}; j++) { int idx = d + j; if (idx >= 0 && idx <= ${i}) { float z = getX(b, r, c, idx); sum += z * z; } } float val = x * ${o}; setOutput(val); } `}},BH=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; int r = coords.y; int c = coords.z; int d = coords.w; bool hasNextCol = d < ${this.outputShape[3]}; bool hasNextRow = c < ${this.outputShape[2]}; vec4 sum = vec4(0.); vec4 xFragAtOutputCoords = getX(b, r, c, d); vec4 xAtOutputCoords = vec4( getChannel(xFragAtOutputCoords, vec2(c, d)), hasNextCol ? getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0, hasNextRow ? getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0, (hasNextRow && hasNextCol) ? getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 ); int firstChannel = d - ${s}; vec2 cache = vec2(0.); if(firstChannel >= 0){ vec4 firstChannelFrag = getX(b, r, c, firstChannel); cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel)); if(hasNextRow){ cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel)); } } ivec2 depth = ivec2(d, d + 1); for (int j = - ${s}; j <= ${s}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i})); bool depthInRange = aboveLowerBound.x && belowUpperBound.x; bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y; if(depthInRange || depthPlusOneInRange){ vec4 z = vec4(0.); vec4 xFragAtCurrentDepth; z.xz = cache.xy; if(depthPlusOneInRange && hasNextCol){ xFragAtCurrentDepth = idx.y != d ? getX(b, r, c, idx.y) : xFragAtOutputCoords; z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y)); if(hasNextRow){ z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y)); } } cache.xy = z.yw; sum += z * z; } } vec4 result = xAtOutputCoords * ${o}; setOutput(result); } `}},VH=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=ee().getBool("WEBGL_PACK_NORMALIZATION")?new BH(r.shape,s,i,o,l):new WH(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},jH={kernelName:Ru,backendName:"webgl",kernelFunc:VH},UH=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; float result = 0.0; for (int d = 0; d < ${this.depth}; ++d) { int depthBegin = int(max(0.0, float(d - ${t}))); int depthEnd = int(min(float(${this.depth}), float(d + ${t} + 1))); const int MIN_DEPTH_BEGIN = 0; const int MAX_DEPTH_END = ${this.depth}; float norm = 0.0; for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = float(${a}) * norm + float(${n}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${a}) * float(${r}) * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${r}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutput(result); } `}},HH=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=a,p=new UH(r.shape,o,l,u,d);return n.runWebGLProgram(p,[r,s,i],r.dtype)},GH={kernelName:Qp,backendName:"webgl",kernelFunc:HH};function qH(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=ye({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Ci(i,e.dtype,"max",a),l=ye({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function mw(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=R.getAxesPermutation(u,o),p=d!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(p){if(c){let g=n.texData.get(h.dataId).values,x=new Array(o);for(let v=0;v`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=R.computePool2DInfo(r.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return Wn({inputs:{x:r},backend:n});let p=new wd(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var eG={kernelName:Fs,backendName:"webgl",kernelFunc:QH};function tG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,d=[1,1,1],p=R.computePool3DInfo(r.shape,s,i,d,o,u,l),c=new w1(p,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var nG={kernelName:Mu,backendName:"webgl",kernelFunc:tG},aG=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${r}; wR += ${a}) { float dyR = float(dyRCorner + wR) / ${t}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${s}; wC++) { float dyC = float(dyCCorner + wC) / ${n}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wR * ${s} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},rG=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,p=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${d}, ${p}, ${c}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${o}; wD += ${r}) { float dyD = float(dyDCorner + wD) / ${t}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${l}; wR += ${s}) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${u}; wC += ${i}) { float dyC = float(dyCCorner + wC) / ${a}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); int maxPosValue = ${h} - int(getMaxPos(batch, idyD, idyR, idyC, ch)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wD * ${l} * ${u} + wR * ${u} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function sG(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=a,p=[1,1,1],c=R.computePool3DInfo(i.shape,o,l,p,u,d),h=new w1(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new rG(c),A=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var iG={kernelName:tc,backendName:"webgl",kernelFunc:sG};function oG(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;$l([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:p}=a,c=R.computePool2DInfo(o.shape,l,u,1,d,p),h=!0,m=new wd(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),A=new aG(c),y=n.runWebGLProgram(A,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var lG={kernelName:ec,backendName:"webgl",kernelFunc:oG};function uG(e,t,n,a){let r=new wd(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new wd(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var dG={kernelName:nc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];k.assert(R.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=R.computePool2DInfo(a.shape,r,s,u,i),[p,c]=uG(a,o,d,l);return[p,c]}};function pG(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=ye({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Ci(i,"float32","mean",a),l=ye({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var cG={kernelName:$s,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=k.parseAxisParam(s,a.shape),u=l,d=R.getAxesPermutation(u,o),p=d!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(c){let x=i.texData.get(m.dataId).values,w=new Array(o);for(let N=0;Nu[0]+e[d]+u[1]);let a=e.length,r=lt(a),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=` int start = ${s}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start) { outC = start * 2 - outC - ${l}; } else if(outC >= end) { outC = (end - 1) * 2 - outC + ${l}; } setOutput(getX(outC - start)); } `;return}this.userCode=` ${r} start = ${r}(${s}); ${r} end = ${r}(${i}); void main() { ${r} outC = getOutputCoords(); for (int i = 0; i < ${a}; i++) { if (outC[i] < start[i]) { outC[i] = start[i] * 2 - outC[i] - ${l}; } else if(outC[i] >= end[i]) { outC[i] = (end[i] - 1) * 2 - outC[i] + ${l}; } } ${r} coords = outC - start; setOutput(getX(${o})); } `}},bG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=lt(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=fn("rc",a),l=fn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,c="";if(a===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${p}; } else if (source >= end) { source = (end - 1) * 2 - source + ${p}; } source -= start; `;c=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${d}); ${o[a-1]} += 1; if(${u}) { ${h} result[1] = getChannel(getX(${l.join()}), ${d}); } `}else{let h=` ${r} source = rc; ${r} lt = ${r}(lessThan(source, start)); ${r} gte = ${r}(greaterThanEqual(source, end)); ${r} orig = 1 - (lt + gte); source = orig * source + lt * (start * 2 - source - ${p}) + gte * ((end - 1) * 2 - source + ${p}); source -= start; `;c=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${d}); ${o[a-1]} += 1; if(${u}) { ${h} result[1] = getChannel(getX(${l.join()}), ${d}); } rc = outputLoc; ${o[a-2]} += 1; if(${o[a-2]} < ${this.outputShape[a-2]}) { ${h} result[2] = getChannel(getX(${l.join()}), ${d}); ${o[a-1]} += 1; if(${u}) { ${h} result[3] = getChannel(getX(${l.join()}), ${d}); } } `}this.userCode=` const ${r} start = ${r}(${s}); const ${r} end = ${r}(${i}); void main() { ${r} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${c} setOutput(result); } `}},vG=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bG(a.shape,r,s):new xG(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},wG={kernelName:Os,backendName:"webgl",kernelFunc:vG},kG=`if (b == 0.0) return NAN; return mod(a, b);`,IG=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); `+kh+` return result; `,SG=nn({opSnippet:kG,packedOpSnippet:IG}),NG={kernelName:Do,backendName:"webgl",kernelFunc:SG},TG=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=` uniform float seed; void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; float r = random(seed); float cdf = 0.0; for (int i = 0; i < ${t-1}; i++) { cdf += getProbs(batch, i); if (r < cdf) { setOutput(float(i)); return; } } // If no other event happened, last event happened. setOutput(float(${t-1})); } `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},EG=` if (a == b) { return 1.0; }; return a / b;`,CG=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; if(a.x == b.x) { result.x = 1.; } if(a.y == b.y) { result.y = 1.; } if(a.z == b.z) { result.z = 1.; } if(a.w == b.w) { result.w = 1.; } return result; `,Aw=nn({opSnippet:EG,packedOpSnippet:CG,checkOutOfBounds:!0}),RG={kernelName:vs,backendName:"webgl",kernelFunc:Aw},yw="return a - b;",gw=nn({opSnippet:yw,packedOpSnippet:yw,supportsComplex:!0,cpuKernelImpl:TW}),MG={kernelName:ei,backendName:"webgl",kernelFunc:gw};function xw(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=mw({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),u=ye({inputs:{x:o},backend:n,attrs:{shape:l}}),d=gw({inputs:{a:r,b:u},backend:n}),p=dw({inputs:{x:d},backend:n}),c=Nh({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=ye({inputs:{x:c},backend:n,attrs:{shape:l}}),m=Aw({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var FG={kernelName:Js,backendName:"webgl",kernelFunc:xw};function $G(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:xw({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],d=l.shape[1],p=new TG(u,d,s),c=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var DG={kernelName:ac,backendName:"webgl",kernelFunc:$G},bw="return -x;";function zG(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=xW(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Pl(a.shape,bw):r=new Vr(a.shape,bw),n.runWebGLProgram(r,[a],a.dtype)}var OG={kernelName:zo,backendName:"webgl",kernelFunc:zG},_G=Ua.nonMaxSuppressionV3Impl;function PG(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:p}=_G(u,d,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var LG={kernelName:_o,backendName:"webgl",kernelFunc:PG},WG=Ua.nonMaxSuppressionV4Impl;function BG(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=WG(d,p,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var VG={kernelName:Po,backendName:"webgl",kernelFunc:BG},jG=Ua.nonMaxSuppressionV5Impl;function UG(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:A,selectedScores:y}=jG(d,p,c,h,m,f);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var HG={kernelName:Lo,backendName:"webgl",kernelFunc:UG},GG=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${a}), float(${n}), float(index == coords.y))); } `}},qG=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=k.sizeFromShape(r.shape),u=new GG(l,s,i,o),d=ye({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(u,[d],r.dtype);n.disposeIntermediateTensorInfo(d);let c=[...r.shape,s],h=ye({inputs:{x:p},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(p),h},XG={kernelName:Ps,backendName:"webgl",kernelFunc:qG};function Mh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Id({inputs:{input:a},backend:n}),s=Mh({inputs:{x:r},backend:n}),i=Rh({inputs:{input:a},backend:n}),o=Mh({inputs:{x:i},backend:n}),l=jr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return N1({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var KG={kernelName:al,backendName:"webgl",kernelFunc:Mh};function vw(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=Id({inputs:{input:a},backend:n}),s=vw({inputs:{x:r},backend:n}),i=Rh({inputs:{input:a},backend:n}),o=Mh({inputs:{x:i},backend:n}),l=jr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return N1({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var ZG={kernelName:Wo,backendName:"webgl",kernelFunc:vw};function YG(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return S1({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let p=S1({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),u=ew({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var JG={kernelName:Bo,backendName:"webgl",kernelFunc:YG},QG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=lt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` int start = ${s}; int end = ${i}; uniform float value; void main() { int outC = getOutputCoords(); if (outC < start || outC >= end) { setOutput(value); } else { setOutput(getX(outC - start)); } } `;return}this.userCode=` ${r} start = ${r}(${s}); ${r} end = ${r}(${i}); uniform float value; void main() { ${r} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(value); } else { ${r} coords = outC - start; setOutput(getX(${o})); } } `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},eq=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=lt(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=fn("rc",a),l=fn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1; if(${u}) { `,a===1?"":`} rc = outputLoc; ${o[a-2]} += 1; if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1; if(${u}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},ww=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eq(r.shape,s,i):new QG(r.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[r],r.dtype,l)},tq={kernelName:Ls,backendName:"webgl",kernelFunc:ww},nq=` if(a < 0.0 && floor(b) < b){ return NAN; } if (b == 0.0) { return 1.0; } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); `,aq=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); vec4 result = multiplier * pow(abs(a), b); // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS bvec4 isExpZero = equal(b, vec4(0.0)); result.r = isExpZero.r ? 1.0 : result.r; result.g = isExpZero.g ? 1.0 : result.g; result.b = isExpZero.b ? 1.0 : result.b; result.a = isExpZero.a ? 1.0 : result.a; vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b)); `+kh+` return result; `,rq=nn({opSnippet:nq,packedOpSnippet:aq}),sq={kernelName:Ws,backendName:"webgl",kernelFunc:rq};function iq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=k.parseAxisParam(s,r.shape),d=u,p=R.getAxesPermutation(d,o),c=r;p!=null&&(c=mn({inputs:{x:r},backend:n,attrs:{perm:p}}),d=R.getInnerMostAxes(d.length,o),l.push(c)),R.assertAxesAreInnerMostDims("prod",d,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:A,outDtype:y}=bW(c.shape,c.dtype,m,d);h=n.makeTensorInfo(A,y,f)}else{let[m,f]=R.computeOutAndReduceShapes(c.shape,d),A=k.sizeFromShape(f),y=ye({inputs:{x:c},backend:n,attrs:{shape:[-1,A]}}),g=Ac(r.dtype),x=Ci(y,g,"prod",n);h=ye({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=R.expandShapeToKeepDim(h.shape,u);h=ye({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var oq={kernelName:Vo,backendName:"webgl",kernelFunc:iq},kw=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=vW(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},lq={kernelName:Fu,backendName:"webgl",kernelFunc:kw},uq="return 1.0 / x;",dq=Xe({opSnippet:uq}),pq={kernelName:jo,backendName:"webgl",kernelFunc:dq},cq=wa+` return (x < 0.0) ? 0.0 : x; `,hq=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,fq=Xe({opSnippet:cq,packedOpSnippet:hq}),mq={kernelName:Vs,backendName:"webgl",kernelFunc:fq},Aq=wa+` return (x < 0.0) ? 0.0 : min(6.0, x); `,yq=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,gq=Xe({opSnippet:Aq,packedOpSnippet:yq}),xq={kernelName:Us,backendName:"webgl",kernelFunc:gq},bq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/d[0]}, ${u[1]/d[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${p}; // Compute the four integer indices. ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0))); ivec2 sourceCeilRC = ivec2( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d); float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d); float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d); float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d); vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC); float top = topLeft + (topRight - topLeft) * fracRC.y; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; float newValue = top + (bottom - top) * fracRC.x; setOutput(newValue); } `}},vq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/d[0]}, ${u[1]/d[1]}, ${u[1]/d[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, ${o}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${p}; // Compute the four integer indices. ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0))); ivec3 sourceCeilRC = ivec3( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${l-1}; bool hasNextRow = coords.z < ${n-1}; // In parallel, construct four corners for all four components in // packed 2x2 cell. vec4 topLeft = vec4( getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 bottomLeft = vec4( getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 topRight = vec4( getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0); vec4 bottomRight = vec4( getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0); vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC); vec4 top = mix(topLeft, topRight, fracRC.yyzz); vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz); vec4 newValue = mix(top, bottom, fracRC.x); setOutput(newValue); } `}};function wq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,d=ee().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new vq(r.shape,l,u,s,i):new bq(r.shape,l,u,s,i);return n.runWebGLProgram(d,[r],"float32")}var kq={kernelName:js,backendName:"webgl",kernelFunc:wq},Iq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],p=1/u,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${u}); const float widthScale = float(${d}); const float invHeightScale = float(${p}); const float invWidthScale = float(${c}); const int winHeight = int(${h}); const int winWidth = int(${m}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(startRLerp - float(winHeight / 2)); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(startCLerp - float(winWidth / 2)); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${s}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${i}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); int bottomDxRIndex = int(min(ceil(dxR), ${a-1}.0)); float dxRLerp = dxR - float(topDxRIndex); float inverseDxRLerp = 1.0 - dxRLerp; float dxC = float(dyC) * widthScale; int leftDxCIndex = int(floor(dxC)); int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0)); float dxCLerp = dxC - float(leftDxCIndex); float inverseDxCLerp = 1.0 - dxCLerp; if (r == topDxRIndex && c == leftDxCIndex) { // topLeft accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp; } if (r == topDxRIndex && c == rightDxCIndex) { // topRight accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp; } if (r == bottomDxRIndex && c == leftDxCIndex) { // bottomLeft accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp; } if (r == bottomDxRIndex && c == rightDxCIndex) { // bottomRight accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp; } } } // End loop over dy setOutput(accumulator); } `}};function Sq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Iq(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Nq={kernelName:ic,backendName:"webgl",kernelFunc:Sq},Tq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/d[0]}, ${u[1]/d[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${c}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } `}},Eq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/d[0]}, ${u[1]/d[1]}, ${u[1]/d[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, ${o}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${c}; // Compute the coordinators of nearest neighbor point. ivec3 sourceNearestRC = ivec3( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p}))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${l-1}; bool hasNextRow = coords.z < ${n-1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0); setOutput(newValue); } `}};function Cq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,d=ee().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Eq(r.shape,l,u,s,i):new Tq(r.shape,l,u,s,i);return n.runWebGLProgram(d,[r],r.dtype)}var Rq={kernelName:$u,backendName:"webgl",kernelFunc:Cq},Mq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],p=1/u,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${u}); const float widthScale = float(${d}); const float invHeightScale = float(${p}); const float invWidthScale = float(${c}); const int winHeight = int(${h}); const int winWidth = int(${m}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(floor(startRLerp - float(winHeight / 2))); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(floor(startCLerp - float(winWidth / 2))); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${s}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${i}) { continue; } float sourceFracRow = float(${o[0]}) * (float(dyR) / float(${l[0]})); float sourceFracCol = float(${o[1]}) * (float(dyC) / float(${l[1]})); int sourceNearestRow = int(min( float(int(${a}) - 1), ${n} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${r}) - 1), ${n} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function Fq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Mq(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var $q={kernelName:sc,backendName:"webgl",kernelFunc:Fq},Dq=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); } `;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=lt(n);this.userCode=` void main() { ${s} coords = getOutputCoords(); setOutput(getX(${r})); } `}},zq=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL 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sum, float(found))); } `}};function Gq(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:p}=R.calculateShapes(s,r,i),c=[p/u,u];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=ye({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ye({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new Iw(l,o,h.shape.length,m.shape.length,d,c),y=n.runWebGLProgram(A,[m,h,f],m.dtype),g=ye({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),g}var qq={kernelName:Ho,backendName:"webgl",kernelFunc:Gq},Xq=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 1.0) { setOutput(getA(${r})); } else { setOutput(getB(${r})); } } `}};function Kq(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new Xq(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],ua(r.dtype,s.dtype))}var Zq={kernelName:Go,backendName:"webgl",kernelFunc:Kq},Yq=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${R.SELU_SCALEALPHA}; float scale = ${R.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `,Jq=Xe({opSnippet:Yq}),Qq={kernelName:qo,backendName:"webgl",kernelFunc:Jq},eX="return 1.0 / (1.0 + exp(-1.0 * x));",tX=Xe({opSnippet:eX}),nX={kernelName:Ks,backendName:"webgl",kernelFunc:tX},aX=` if (isnan(x)) { return 0.0; } return sign(x); `,rX=Xe({opSnippet:aX}),sX={kernelName:Zo,backendName:"webgl",kernelFunc:rX},iX=Lv+` return sin(x); `,oX=Xe({opSnippet:iX}),lX={kernelName:Xs,backendName:"webgl",kernelFunc:oX},uX=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `,dX=Xe({opSnippet:uX}),pX={kernelName:Ko,backendName:"webgl",kernelFunc:dX},cX=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; bool too_large = x > -threshold; bool too_small = x < threshold; float result; float exp_x = exp(x); if (too_large){ result = x; } else if (too_small){ result = exp_x; } else{ result = log(exp_x + 1.0); } return result; `,hX=Xe({opSnippet:cX}),fX={kernelName:Yo,backendName:"webgl",kernelFunc:hX},mX=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;k.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;yn.disposeIntermediateTensorInfo(y)),A},AX={kernelName:Du,backendName:"webgl",kernelFunc:mX};function yX(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: ${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw: ${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw: ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw: ${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),d=n.readSync(i.dataId)[0],[p,c,h,m,f]=IW(o,a.shape,a.dtype,l,r.dtype,u,d);return[n.makeTensorInfo(c,a.dtype,p),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(A=>Number(A)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var gX={kernelName:oc,backendName:"webgl",kernelFunc:yX};function xX(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,d,p]=SW(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(d,a.dtype,u),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var bX={kernelName:lc,backendName:"webgl",kernelFunc:xX};function vX(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,strides:d,outputSize:p}=R.calculateShapes(s,r,o),c=!1,h=new Iw(u,l,r.shape.length,s.shape.length,d,[p,1],c),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=ye({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var wX={kernelName:uc,backendName:"webgl",kernelFunc:vX};function kX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=R.prepareSplitSize(r,s,o),u=r.shape.length,d=new Array(u).fill(0),p=r.shape.slice();return l.map(c=>{let h=[...p];h[o]=c;let m=kd({inputs:{x:r},backend:n,attrs:{begin:d,size:h}});return d[o]+=c,m})}var IX={kernelName:Jo,backendName:"webgl",kernelFunc:kX},SX="return sqrt(x);",NX=Xe({opSnippet:SX}),TX={kernelName:Zs,backendName:"webgl",kernelFunc:NX},EX="return x * x;",CX=Xe({opSnippet:EX}),RX={kernelName:zu,backendName:"webgl",kernelFunc:CX},Sw="return (a - b) * (a - b);",MX=nn({opSnippet:Sw,packedOpSnippet:Sw}),FX={kernelName:Qs,backendName:"webgl",kernelFunc:MX};function $X({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=wa+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,s=new Vr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var DX={kernelName:Rr,backendName:"webgl",kernelFunc:$X},zX=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=lt(n.length),s=lt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${r} begin = ${r}(${e}); ${r} strides = ${r}(${t}); void main() { ${s} coords = getOutputCoords(); setOutput(getX(${i})); } `}};function OX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a,{nonStrided:h,$begin:m,$strides:f,size:A,newShape:y,outShape:g}=un.sliceInfo(r.shape,s,i,o,l,u,d,p,c),x=ye({inputs:{x:r},backend:n,attrs:{shape:y}}),w;if(h){let v=kd({inputs:{x},backend:n,attrs:{begin:m,size:A}});w=ye({inputs:{x:v},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(v)}else if(g.some(v=>v===0))w=n.makeTensorInfo(g,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let v=n.texData.get(x.dataId).values,N=We(x.shape,x.dtype,v),T=NW(g,N,f,m);w=n.makeTensorInfo(g,x.dtype,T.values)}else{let v=new zX(m,f,g);w=n.runWebGLProgram(v,[x],x.dtype)}let b=ye({inputs:{x:w},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),b}var _X={kernelName:Qo,backendName:"webgl",kernelFunc:OX},PX="return tan(x);",LX=Xe({opSnippet:PX}),WX={kernelName:ti,backendName:"webgl",kernelFunc:LX},BX=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,VX=Xe({opSnippet:BX}),jX={kernelName:ni,backendName:"webgl",kernelFunc:VX},UX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(p=>k.decodeString(p)):o,u=We(r.shape,r.dtype,l),d=EW(u,s);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new UX(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var GX={kernelName:Cr,backendName:"webgl",kernelFunc:Nw};function qX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[l,u]=CW(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var XX={kernelName:el,backendName:"webgl",kernelFunc:qX},KX=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${o} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * float(int(float(-inCoord / sz2))) + inCoord; } inCoord = inCoord < -len ? inCoord + sz2 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void main() { ivec4 coords = getOutputCoords(); float outputValue; int batch = coords[0]; int x = coords[2]; int y = coords[1]; int channel = coords[3]; float xf = float(x); float yf = float(y); float a1 = getTransforms(batch, 0); float a2 = getTransforms(batch, 1); float a3 = getTransforms(batch, 2); float b1 = getTransforms(batch, 3); float b2 = getTransforms(batch, 4); float b3 = getTransforms(batch, 5); float c1 = getTransforms(batch, 6); float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = float(${r}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; float mapX = mapCoord(inX, float(${t})); float mapY = mapCoord(inY, float(${e})); if (${i} == 1) { int coordY = int(round(mapY)); int coordX = int(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, channel); } else { float yFloor = floor(mapY); float xFloor = floor(mapX); float yCeil = yFloor + 1.0; float xCeil = xFloor + 1.0; float valueYFloor = (xCeil - mapX) * readWithFillValue(batch, int(yFloor), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yFloor), int(xCeil), channel); float valueYCeil = (xCeil - mapX) * readWithFillValue(batch, int(yCeil), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yCeil), int(xCeil), channel); outputValue = (yCeil - mapY) * valueYFloor + (mapY - yFloor) * valueYCeil; } } setOutput(outputValue); } `}};function ZX(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[d,p,c,h]=r.shape,[m,f]=u!=null?u:[p,c],A=[d,m,f,h],y=new KX(p,c,i,o,l,A);return n.runWebGLProgram(y,[r,s],"float32")}var YX={kernelName:tl,backendName:"webgl",kernelFunc:ZX};function JX(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;$l(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");r=t}else k.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Xl))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Ca(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Xl.className="Sequential";se.registerClass(Xl);function rae(e){return new mr(e)}function sae(e){return new Xl(e)}function iae(e,t){return t==null&&(t={}),tae(e,t)}function M4(e){return a4(e)}function oae(e,t){ma.registerCallbackConstructor(e,t)}var Rn=class extends se.Serializable{getConfig(){return{}}},F4=class extends Rn{apply(e,t=1){return $te(e,t)}};F4.className="elu";se.registerClass(F4);var $4=class extends Rn{apply(e){return Bc(e)}};$4.className="selu";se.registerClass($4);var D4=class extends Rn{apply(e){return Va(e)}};D4.className="relu";se.registerClass(D4);var z4=class extends Rn{apply(e){return W(()=>wl(6,Va(e)))}};z4.className="relu6";se.registerClass(z4);var O4=class extends Rn{apply(e){return e}};O4.className="linear";se.registerClass(O4);var _4=class extends Rn{apply(e){return Sn(e)}};_4.className="sigmoid";se.registerClass(_4);var P4=class extends Rn{apply(e){return zte(e)}};P4.className="hardSigmoid";se.registerClass(P4);var L4=class extends Rn{apply(e){return yi(e)}};L4.className="softplus";se.registerClass(L4);var W4=class extends Rn{apply(e){return Dte(e)}};W4.className="softsign";se.registerClass(W4);var B4=class extends Rn{apply(e){return fi(e)}};B4.className="tanh";se.registerClass(B4);var my=class extends Rn{apply(e,t=-1){return od(e,t)}};my.className="softmax";se.registerClass(my);var V4=class extends Rn{apply(e,t=-1){return Dc(e,t)}};V4.className="logSoftmax";se.registerClass(V4);var j4=class extends Rn{apply(e,t=1){return W(()=>Sn(e.mul(t)).mul(e))}};j4.className="swish";se.registerClass(j4);var U4=class extends Rn{apply(e){return W(()=>P(e,fi(yi(e))))}};U4.className="mish";se.registerClass(U4);function Kr(e){return e.getClassName()}function Ay(e,t={}){return Cd(e,se.SerializationMap.getMap().classNameMap,t,"activation")}function Zr(e){if(e==null){let t={};return t.className="linear",t.config={},Ay(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Ay(t)}else return e instanceof Rn?e:Ay(e)}function yy(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var H4=class extends se.Serializable{},Wd=class extends H4{constructor(e){super();yy(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return W(()=>{let t=Mt([1]);return this.hasL1&&(t=oe(t,ke(P(this.l1,Lt(e))))),this.hasL2&&(t=oe(t,ke(P(this.l2,Dd(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Wd.className="L1L2";se.registerClass(Wd);function lae(e){return yy(e),new Wd({l1:e!=null?e.l1:null,l2:0})}function uae(e){return yy(e),new Wd({l2:e!=null?e.l2:null,l1:0})}var G4={l1l2:"L1L2"};function ut(e){return D1(e)}function q4(e,t={}){return Cd(e,se.SerializationMap.getMap().classNameMap,t,"regularizer")}function yt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in G4?G4[e]:e,config:{}};return q4(t)}else return e instanceof H4?e:q4(e)}var gy=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=_e(e);let n=Va(e);return this.maxValue!=null&&(n=Nn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};gy.className="ReLU";se.registerClass(gy);var xy=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=_e(e);return ed(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};xy.className="LeakyReLU";se.registerClass(xy);var by=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=At(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=yt(e.alphaRegularizer),this.alphaConstraint=jt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=rt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a(Rt(t),t==="channelsFirst"?Ye(e,[0,2,3,1]):e))}function X4(e,t){return W(()=>(Rt(t),t==="channelsFirst"?Ye(e,[0,2,3,4,1]):e))}function dae(e,t,n,a=1,r="valid",s,i=1){return W(()=>{if(s==null&&(s=Ia()),Rt(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Ye(e,[0,2,1])),r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Nc(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ta(o,n)),o})}function K4(e,t,n,a=[1,1],r="valid",s,i,o=null){return W(()=>{if(s==null&&(s=Ia()),Rt(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Iy(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Wr.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ye(l,[0,3,1,2])),l})}function pae(e,t,n,a=[1,1,1],r="valid",s,i){return W(()=>{if(s==null&&(s=Ia()),Rt(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=X4(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=mA(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ta(o,n)),s==="channelsFirst"&&(o=Ye(o,[0,4,1,2,3])),o})}var Sy=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Sy.verifyArgs(t),this.rank=e,Kt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Kl(t.kernelSize,e,"kernelSize"),this.strides=Kl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,aa(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=Zr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=At(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=jt(t.biasConstraint),this.biasRegularizer=yt(t.biasRegularizer),this.activityRegularizer=yt(t.activityRegularizer),this.dilationRate=Kl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(qa("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!O1(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Kr(this.activation),useBias:this.useBias,biasInitializer:It(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Vt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Bd=class extends Sy{constructor(e,t){super(e,t);this.kernel=null,Bd.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=At(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=jt(t.kernelConstraint),this.kernelRegularizer=yt(t.kernelRegularizer)}build(e){e=rt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return W(()=>{e=_e(e);let n,a=this.bias==null?null:this.bias.read(),r=_6(this.activation.getClassName());if(r!=null&&this.rank===2)n=K4(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=dae(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=K4(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=pae(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=rt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},Vd=class extends Bd{constructor(e){super(2,e);Vd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!O1(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Vd.className="Conv2D";se.registerClass(Vd);var jd=class extends Bd{constructor(e){super(3,e);jd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};jd.className="Conv3D";se.registerClass(jd);var Ny=class extends Vd{constructor(e){super(e);if(this.inputSpec=[new $t({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=rt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new $t({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return W(()=>{let n=_e(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],d=this.kernelSize[1],p=this.strides[0],c=this.strides[1],h=Za(o,p,u,this.padding),m=Za(l,c,d,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ye(n,[0,2,3,1]));let A=Tc(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Ye(A,[0,3,1,2])),this.bias!=null&&(A=Ta(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=rt(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=Za(t[a],o,s,this.padding),t[r]=Za(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ny.className="Conv2DTranspose";se.registerClass(Ny);var Ty=class extends jd{constructor(e){super(e);if(this.inputSpec=[new $t({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=rt(e),e.length!==5)throw new B("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new $t({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return W(()=>{let n=_e(e);if(n.shape.length!==5)throw new B(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],d=a[i],p=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],A=this.strides[2],y=Za(l,m,p,this.padding),g=Za(u,f,c,this.padding),x=Za(d,A,h,this.padding),w=[r,y,g,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Ye(n,[0,2,3,4,1]));let b=Gb(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(b=Ye(b,[0,4,1,2,3])),this.bias!==null&&(b=Ta(b,this.bias.read(),this.dataFormat)),this.activation!==null&&(b=this.activation.apply(b)),b})}computeOutputShape(e){e=rt(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[a]=Za(t[a],u,i,this.padding),t[r]=Za(t[r],d,o,this.padding),t[s]=Za(t[s],p,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ty.className="Conv3DTranspose";se.registerClass(Ty);var Z4=class extends Bd{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=At(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=yt(t.depthwiseRegularizer),this.depthwiseConstraint=jt(t.depthwiseConstraint),this.pointwiseInitializer=At(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=yt(t.pointwiseRegularizer),this.pointwiseConstraint=jt(t.pointwiseConstraint)}build(e){if(e=rt(e),e.length{e=_e(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ye(e,[0,2,3,1])),n=$A(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ta(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ye(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=It(this.depthwiseInitializer),e.pointwiseInitializer=It(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Vt(this.depthwiseConstraint),e.pointwiseConstraint=Vt(this.pointwiseConstraint),e}};Z4.className="SeparableConv";var Ey=class extends Z4{constructor(e){super(2,e)}};Ey.className="SeparableConv2D";se.registerClass(Ey);var i0=class extends Bd{constructor(e){super(1,e);i0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!O1(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};i0.className="Conv1D";se.registerClass(i0);var Cy=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return W(()=>{if(e=_e(e),this.dataFormat==="channelsLast"){let n=Ph(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ph(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ph(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ph(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Cy.className="Cropping2D";se.registerClass(Cy);var Ry=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Nte(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return W(()=>{let n=_e(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ye(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Ye(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ry.className="UpSampling2D";se.registerClass(Ry);function cae(e,t,n=[1,1],a="valid",r,s){return W(()=>{r==null&&(r=Ia()),Rt(r);let i=Iy(e,r);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=yl(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ye(i,[0,3,1,2])),i})}var My=class extends Sy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=At(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=jt(e.depthwiseConstraint),this.depthwiseRegularizer=yt(e.depthwiseRegularizer)}build(e){if(e=rt(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return W(()=>{e=_e(e);let n=cae(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ta(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=rt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ra(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ra(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=It(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Vt(this.depthwiseRegularizer),e}};My.className="DepthwiseConv2D";se.registerClass(My);function Y4(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function J4(e,t,n,a=!1,r,s,i=!1,o=!1){return W(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Na(2,l));if(t=Ye(t,u),s!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=dn(r,-1)),r=Ye(r,u)),a&&(t=Pn(t,0),r!=null&&(r=Pn(r,0)));let d=[],p,c=n,h=t.shape[0],m=ca(t),f;r!=null&&(f=ca(r));for(let y=0;ye(g,c));if(r==null)p=x[0],c=x[1];else{let w=W(()=>{let b=f[y],v=_n(b).sub(b),N=x[0].mul(b).add(c[0].mul(v)),T=c.map((C,$)=>x[1][$].mul(b).add(C.mul(v)));return{output:N,newStates:T}});p=w.output,c=w.newStates}o&&d.push(p)}let A;return o&&(A=pn(d,1)),[p,A,c]})}var Ya=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new u0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new $t({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Na(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Q1(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return W(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ni.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new $t({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){W(()=>{if(!this.stateful)throw new hr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>Mt([n,a])):this.states_=[Mt([n,this.cell.stateSize])];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>Mt([n,a])):this.states_[0]=Mt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let a=0;aGt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=Y4(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new $t({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Ea){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return W(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=_e(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=J4((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,a);let p=this.returnSequences?u:l;return this.returnState?[p].concat(d):p})}getInitialState(e){return W(()=>{let t=Mt(e.shape);return t=ke(t,[1,2]),t=$d(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?U1(t,[1,n]):t):this.cell.stateSize>1?[U1(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Ya.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ca(a,n);return new e(Object.assign(t,{cell:r}))}};Ya.className="RNN";se.registerClass(Ya);var Ud=class extends qe{},o0=class extends Ud{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=Zr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=At(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=Ul([1,qr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,qr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=rt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return W(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0_n(e),rate:this.dropout,training:a})),0_n(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=Xa(P(e,s),this.kernel.read()):r=Xa(e,this.kernel.read()),this.bias!=null&&(r=Ta(r,this.bias.read())),i!=null&&(n=P(n,i));let o=oe(r,Xa(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Kr(this.activation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),recurrentConstraint:Vt(this.recurrentConstraint),biasConstraint:Vt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};o0.className="SimpleRNNCell";se.registerClass(o0);var Fy=class extends Ya{constructor(e){e.cell=new o0(e),super(e)}call(e,t){return W(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};Fy.className="SimpleRNN";se.registerClass(Fy);var l0=class extends Ud{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=Zr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Zr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=At(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=Ul([1,qr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,qr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=rt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return W(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0_n(e),rate:this.dropout,training:n,count:3})),0_n(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};$y.className="GRU";se.registerClass($y);var Hd=class extends Ud{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=Zr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Zr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=At(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=Ul([1,qr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,qr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=rt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends fa{apply(i,o){let l=r.apply([s]),u=new Wh().apply([s]),d=r.apply([s*2]);return q6(q6(l,u),d)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return W(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0_n(e),rate:this.dropout,training:n,count:4})),0_n(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Dy.className="LSTM";se.registerClass(Dy);var u0=class extends Ud{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return W(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{$i(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ca(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return ey(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;sK6(t(),n),i=()=>zd(s,t,a);return!r||r<=1?Gt(i().clone()):Array(r).fill(void 0).map(i).map(o=>Gt(o.clone()))}var hae=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return W(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=Mt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){W(()=>{if(!this.stateful)throw new hr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. 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Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends fa{apply(d,p){let c=l.apply([u]),h=On([u]),m=l.apply([u*2]);return j1([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return W(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0_n(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ie,ne)=>!ie||!ie[ne]?Y:P(ie[ne],Y),u=l(a,o,0),d=l(a,o,1),p=l(a,o,2),c=l(a,o,3);0_n(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),A=l(r,h,2),y=l(r,h,3),g=3,[x,w,b,v]=qt(this.kernel.read(),i,g),[N,T,C,$]=this.useBias?qt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,N,this.padding),d=this.inputConv(d,w,T,this.padding),p=this.inputConv(p,b,C,this.padding),c=this.inputConv(c,v,$,this.padding);let[z,_,V,U]=qt(this.recurrentKernel.read(),i,g);m=this.recurrentConv(m,z),f=this.recurrentConv(f,_),A=this.recurrentConv(A,V),y=this.recurrentConv(y,U);let j=this.recurrentActivation.apply(oe(u,m)),X=this.recurrentActivation.apply(oe(d,f)),G=oe(P(X,s),P(j,this.activation.apply(oe(p,A)))),te=P(this.recurrentActivation.apply(oe(c,y)),this.activation.apply(G));return[te,te,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=hae(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=or(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ta(r,n,this.dataFormat):r}recurrentConv(e,t){return or(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};d0.className="ConvLSTM2DCell";se.registerClass(d0);var zy=class extends Q4{constructor(e){let t=new d0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};zy.className="ConvLSTM2D";se.registerClass(zy);var p0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=_e(e);if(0K6(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};p0.className="Dropout";se.registerClass(p0);var Oy=class extends p0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Oy.className="SpatialDropout1D";se.registerClass(Oy);var _y=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Kt(this.units,"units"),this.activation=Zr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=jt(e.kernelConstraint),this.biasConstraint=jt(e.biasConstraint),this.kernelRegularizer=yt(e.kernelRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.activityRegularizer=yt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=rt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=rt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=_e(e),a=_6(this.activation.getClassName()),r;return a!=null?r=Xa(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=Xa(n,this.kernel.read()),this.bias!=null&&(r=Ta(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let 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qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=_e(e);return zd(()=>Lh(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Jy.className="GaussianNoise";se.registerClass(Jy);var Qy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=_e(e);return this.rate>0&&this.rate<1?zd(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(Lh(n.shape,1,a))},()=>n,t.training||!1):n})}};Qy.className="GaussianDropout";se.registerClass(Qy);var e2=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||_e(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return W(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return zd(()=>{let a=_e(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=Pr(kl(n),this.rate);o=Fd(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>_e(e),t.training||!1)}return e})}};e2.className="AlphaDropout";se.registerClass(e2);function qd(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=Lb(e,t,n,a,r,s);else if(e.rank===3)i=Wb(e,t,n,a,r,s);else if(e.rank===4)i=Bb(e,t,n,a,r,s);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function mae(e,t,n,a,r=.001){return W(()=>{let s=Oc(e,a),i=s.mean,o=s.variance;return[qd(e,i,o,n,t,r),i,o]})}function Aae(e,t,n,a,r=.001){return W(()=>{let s=Oc(e,a),i=s.mean,o=s.variance,l=[];for(let h of Na(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let u=i.reshape(l),d=o.reshape(l),p=t==null?null:t.reshape(l),c=n==null?null:n.reshape(l);return[qd(e,u,d,c,p,r),i,o]})}function yae(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Na(0,e.rank-1))?mae(e,t,n,a,r):Aae(e,t,n,a,r)}var t2=class extends qe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=At(e.betaInitializer||"zeros"),this.gammaInitializer=At(e.gammaInitializer||"ones"),this.movingMeanInitializer=At(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=At(e.movingVarianceInitializer||"ones"),this.betaConstraint=jt(e.betaConstraint),this.gammaConstraint=jt(e.gammaConstraint),this.betaRegularizer=yt(e.betaRegularizer),this.gammaRegularizer=yt(e.gammaRegularizer)}build(e){e=rt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new $t({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return W(()=>{let n=t.training==null?!1:t.training,a=_e(e),r=a.shape,s=r.length,i=Na(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Ri(1,s);l[o]=r[o];let u=i.slice();u.sort();let d=!k.arraysEqual(u,Na(0,s).slice(0,s-1)),p=()=>{if(d){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):null;return qd(a,A,y,g,x,this.epsilon)}else return qd(a,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[c,h,m]=yae(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(A,y,g)=>{W(()=>{let x=1-g,w=A.read(),b=w.sub(y).mul(x);A.write(w.sub(b))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),c})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:It(this.betaInitializer),gammaInitializer:It(this.gammaInitializer),movingMeanInitializer:It(this.movingMeanInitializer),movingVarianceInitializer:It(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:Vt(this.betaConstraint),gammaConstraint:Vt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};t2.className="BatchNormalization";se.registerClass(t2);var n2=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=At(e.betaInitializer||"zeros"),this.gammaInitializer=At(e.gammaInitializer||"ones"),this.betaRegularizer=yt(e.betaRegularizer),this.gammaRegularizer=yt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=rt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==Hr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=_e(e),a=n.shape,r=a.length;return W(()=>{let s=!0,{mean:i,variance:o}=Oc(n,this.axis,s),l=Ri(1,r);for(let m of this.axis)l[m]=a[m];let u=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,d=u(this.gamma.read()),p=u(this.beta.read()),c=[],h=[];for(let m=0;m{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two 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length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new $t({ndim:4})]}computeOutputShape(e){e=rt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return W(()=>gae(_e(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};a2.className="ZeroPadding2D";se.registerClass(a2);function c0(e,t,n,a,r,s){return W(()=>{Rt(r),B6(s),aa(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Ia()),s==null&&(s="max"),e=Iy(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=nd(e,t,n,o):i=Yu(e,t,n,o),r==="channelsFirst"&&(i=Ye(i,[0,3,1,2])),i})}function e8(e,t,n,a,r,s){return W(()=>{Rt(r),B6(s),aa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ia()),s==null&&(s="max"),e=X4(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=TA(e,t,n,o):i=pA(e,t,n,o),r==="channelsFirst"&&(i=Ye(i,[0,4,1,2,3])),i})}var t8=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Kt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,aa(this.padding),this.inputSpec=[new $t({ndim:3})]}computeOutputShape(e){e=rt(e);let t=Ra(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return W(()=>{this.invokeCallHook(e,t),e=$d(_e(e),2);let n=this.poolingFunction(_e(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ja(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},r2=class extends t8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),aa(a),c0(e,t,n,a,r,"max")}};r2.className="MaxPooling1D";se.registerClass(r2);var s2=class extends t8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),aa(a),c0(e,t,n,a,r,"avg")}};s2.className="AveragePooling1D";se.registerClass(s2);var n8=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),aa(this.padding),this.inputSpec=[new $t({ndim:4})]}computeOutputShape(e){e=rt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ra(t,this.poolSize[0],this.padding,this.strides[0]),n=Ra(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return W(()=>(this.invokeCallHook(e,t),this.poolingFunction(_e(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},i2=class extends n8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),aa(a),c0(e,t,n,a,r,"max")}};i2.className="MaxPooling2D";se.registerClass(i2);var o2=class extends n8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),aa(a),c0(e,t,n,a,r,"avg")}};o2.className="AveragePooling2D";se.registerClass(o2);var a8=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),aa(this.padding),this.inputSpec=[new $t({ndim:5})]}computeOutputShape(e){e=rt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ra(t,this.poolSize[0],this.padding,this.strides[0]),n=Ra(n,this.poolSize[1],this.padding,this.strides[1]),a=Ra(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return W(()=>(this.invokeCallHook(e,t),this.poolingFunction(_e(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},l2=class extends a8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),aa(a),e8(e,t,n,a,r,"max")}};l2.className="MaxPooling3D";se.registerClass(l2);var u2=class extends a8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),aa(a),e8(e,t,n,a,r,"avg")}};u2.className="AveragePooling3D";se.registerClass(u2);var r8=class extends qe{constructor(e){super(e);this.inputSpec=[new $t({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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written.`);n.tensor=t,Gt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,a)=>this.write(n,t[a]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let a=0;a=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,ca(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,a=e.map(o=>(n+=o,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of 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a=Xd(this.elementShape,this.tensors,e);return W(()=>{let r=this.tensors.map(s=>H(s,a));return pn(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=Xd(this.elementShape,this.tensors,e),a=this.tensors.pop();return Aa(a.shape,e,"TensorList shape mismatch: "),H(a,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Aa(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Gt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. 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r=e.shape.slice(1);Aa(r,t,"TensorList shape mismatch: ");let s=ca(e);return new Kd(s,t,a)}function kse(e,t,n){return new Kd([],e,t,n)}function Ise(e,t,n,a){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(a!=null&&a!==-1&&r>=a)throw new Error(`Max index must be < array size (${r} vs. ${a})`);let s=new Kd([],n,e.dtype,a),i=ca(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function Sse(e,t,n){let a=0,r=t.map(d=>(a+=d,a));if(a!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=M2(s,n),o=a===0?0:e.size/a,l=W(()=>{let d=[];e=H(e,[1,a,o]);for(let p=0;p{switch(e.op){case"If":case"StatelessIf":{let a=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await 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a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=I("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let a=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(a.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return[Se(r.size(),"int32")]}case"TensorArrayCloseV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(a.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let 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Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new $2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=U8(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. 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u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Ar(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!gn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!gn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Bn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&k.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Bn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Bn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Kse=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Zse="?tfjs-format=file",Yse="model.json",G8=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Kse}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=In.browserHTTPRequest(e,this.loadOptions);else{let t=In.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(In.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=In.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new $2(O8.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=O8.Instance.transformGraph(e.modelInitializer);this.initializer=new $2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=In.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Le)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Dt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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return n.set(e,r.value),r.value}function nie(e,t=K8){return X8(e,t)}function X8(e,t,n=new Set){let a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(Zl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=X8(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function K8(e){return e===null?null:Zl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function Z8(e,t){let n=new Map;A0(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(k.isPromise(r)){let s=await r;n.set(a,s)}}return A0(e,t,n)}function Zl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Le))}function aie(e){return e==null||rie(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Le||k.isTypedArray(e)}function rie(e){return e===null||typeof e!="object"&&typeof e!="function"}function sie(e){return tie(e,iie)}function iie(e){return e instanceof Le?{value:e.clone(),recurse:!1}:Zl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var Y8=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},D2=class extends Y8{constructor(){super(D2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let a=0;at===!0)}rowMajorBatch(e,t=!0){return new fie(this,e,t)}columnMajorBatch(e,t=!0,n=K8){return this.rowMajorBatch(e,t).map(a=>nie(a,n))}concatenate(e,t){return new ek(J8([this,e]),t)}take(e){return e<0||e==null?this:new hie(this,e)}skip(e){return e<0||e==null?this:new cie(this,e)}prefetch(e){return new tk(this,e)}shuffle(e,t){return new bie(this,e,t)}serial(){return new pie(this)}},uie=class extends Zt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:sie(e),done:!1}}},die=class extends Zt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},pie=class extends Zt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},cie=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},fie=class extends Zt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},mie=class extends Zt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},Aie=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=xa.getTensorsInContainer(e.value),n=this.transform(e.value),a=xa.getTensorsInContainer(n);for(let r of t)xa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},yie=class extends Zt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Q8=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=xa.getTensorsInContainer(e.value),n=await this.transform(e.value),a=xa.getTensorsInContainer(n);for(let r of t)xa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},O2=class extends Zt{constructor(){super();this.outputQueue=new D2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},gie=class extends O2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=xa.getTensorsInContainer(e.value),n=this.transform(e.value),a=xa.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)xa.isTensorInList(r,a)||r.dispose();return!0}},ek=class extends Zt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Jr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Jr||(Jr={}));var xie=class extends Zt{constructor(e,t=Jr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof Zt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await Z8(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Jr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Jr.SHORTEST:return{value:null,done:!0};case Jr.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},tk=class extends Zt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new Y8(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},bie=class extends tk{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=eie.alea(n||k.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Yl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is ${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Vn(async()=>(await n.iterator()).columnMajorBatch(e,t,kie),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Vn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Vn(async()=>(await t.iterator()).filter(a=>W(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Vn(async()=>(await t.iterator()).map(n=>W(()=>e(n))),this.size)}mapAsync(e){let t=this;return Vn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Vn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Vn(async()=>{let a=z2(async()=>({value:await t.iterator(),done:!1}));return oie(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=Qse.alea(t||k.now().toString());return Vn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Vn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Yl.MAX_BUFFER_SIZE=1e4;function Vn(e,t=null){return new class extends Yl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function vie(e){return Vn(async()=>J8(e),e.length)}function wie(e){if(!Zl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await Z8(e,a=>{if(a instanceof Yl)return{value:a.iterator(),recurse:!1};if(Zl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return lie(n,Jr.SHORTEST)},t)}function kie(e){if(e===null)return null;let t=e[0];return aie(t)?{value:Iie(e),recurse:!1}:{value:null,recurse:!0}}function Iie(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Le?pn(e):da(e)}var nk=class extends Yl{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` `).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},y0='"',Zd=Symbol("out"),ak=Symbol("field"),g0=Symbol("quote"),_2=Symbol("quoteafterquote"),rk=Symbol("quoteinquote"),sk=class extends Yl{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new nk(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(ee().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new ik(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),da(n,t)}},ok=class extends Zt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ct([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=va([s,r,o,i],[1,4])}else this.cropBox=va([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(ee().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new ok(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=pi.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return W(()=>{let t=dn(me(e,"float32"),0),n;n=Ge.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return H(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},lk=class{},uk=class extends Zt{split(e){return new Sie(this,e)}},Sie=class extends 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Vie=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],jie=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Uie=[33,133,362,263,1,78,308],Roe=Vie.map(e=>Qd[e]),Moe=jie.map(e=>Qd[e]),Foe=Uie.map(e=>Qd[e]);var B2=Ja.leftEyeLower0,V2=Ja.rightEyeLower0,eu={leftBounds:[B2[0],B2[B2.length-1]],rightBounds:[V2[0],V2[V2.length-1]]},k0={count:468,mouth:13,symmetryLine:[13,Ja.midwayBetweenEyes[0]]},Sk={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},tu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function I0(e,t,n,a){for(let r=0;r[s[0]/this.meshSize*(p[0]-this.meshSize/2),s[1]/this.meshSize*(p[1]-this.meshSize/2),p[2]]),o=a!==0?w0(a,[0,0]):v0,l=a!==0?i.map(p=>[...bk(p,o),p[2]]):i,u=a!==0?xk(r):v0,d=[...Jl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(p=>[Math.round(p[0]+Qr(d,u[0])),Math.round(p[1]+Qr(d,u[1])),Math.round(p[2])])}getLeftToRightEyeDepthDifference(t){let n=t[eu.leftBounds[0]][2],a=t[eu.rightBounds[0]][2];return n-a}getEyeBox(t,n,a,r,s=!1){let i=b0(x0(P2([t[a],t[r]]),this.irisEnlarge)),o=Jd(i),l=Ge.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&la.flags.IS_BROWSER&&(l=Ge.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,a,r=!1){let s=[];for(let i=0;i{let u=i;return l===2?u=r:l===4&&(u=s),[o[0],o[1],u]})}async predict(t,n){let a=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.skipFrame)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.skipFrame&&this.skipped++,!n.skipFrame||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxDetected)){this.storedBoxes=[],this.detectedFaces=0;for(let i of r.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks.arraySync(),confidence:i.confidence});this.storedBoxes.length>0&&(a=!0)}if(a){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=W(()=>this.storedBoxes.map((i,o)=>{let l,u=0,d;if(n.face.detector.rotation&&n.face.mesh.enabled&&la.flags.IS_BROWSER){let[x,w]=i.landmarks.length>=k0.count?k0.symmetryLine:Sk.symmetryLine;u=L2(i.landmarks[x],i.landmarks[w]);let b=Jl({startPoint:i.startPoint,endPoint:i.endPoint}),v=[b[0]/t.shape[2],b[1]/t.shape[1]],N=Ge.rotateWithOffset(t,u,0,v);d=w0(-u,b),n.face.mesh.enabled?l=Ql({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.meshSize,this.meshSize]).div(255):l=Ql({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.boxSize,this.boxSize]).div(255)}else{d=v0;let x=t.clone();n.face.mesh.enabled?l=Ql({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.meshSize,this.meshSize]).div(255):l=Ql({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{mesh:[],box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:l};let[,p,c]=this.meshDetector.execute(l),h=p.dataSync()[0];if(h=k0.count?k0.symmetryLine:Sk.symmetryLine;u=L2(i.landmarks[x],i.landmarks[w]);let b=Jl({startPoint:i.startPoint,endPoint:i.endPoint}),v=[b[0]/t.shape[2],b[1]/t.shape[1]],N=Ge.rotateWithOffset(t.toFloat(),u,0,v);d=w0(-u,b),l=Ql({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.meshSize,this.meshSize]).div(255)}let g={mesh:A,box:i,faceConfidence:h,boxConfidence:i.confidence,image:l};return this.storedBoxes[o]={...b0(i),confidence:i.confidence,faceConfidence:h},g}));return 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o=r.box?[Math.max(0,r.box.startPoint[0]),Math.max(0,r.box.startPoint[1]),Math.min(e.shape[2],r.box.endPoint[0])-Math.max(0,r.box.startPoint[0]),Math.min(e.shape[1],r.box.endPoint[1])-Math.max(0,r.box.startPoint[1])]:0,l=r.box?[r.box.startPoint[0]/e.shape[2],r.box.startPoint[1]/e.shape[1],(r.box.endPoint[0]-r.box.startPoint[0])/e.shape[2],(r.box.endPoint[1]-r.box.startPoint[1])/e.shape[1]]:[];a.push({confidence:Math.round(100*r.faceConfidence||100*r.boxConfidence||0)/100,boxConfidence:Math.round(100*r.boxConfidence)/100,faceConfidence:Math.round(100*r.faceConfidence)/100,box:o,boxRaw:l,mesh:r.mesh,meshRaw:s,annotations:i,image:r.image}),r.coords&&r.coords.dispose()}return a}async function G2(e){return!Tt[0]&&e.face.enabled||!Tt[1]&&e.face.mesh.enabled||!Tt[2]&&e.face.iris.enabled?(Tt=await 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ep=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Mk=ep.length,tp=ep.reduce((e,t,n)=>(e[t]=n,e),{}),qie=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Xie=qie.map(([e,t])=>[tp[e],tp[t]]),Fk=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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n)i.dispose();let r=await Pk(a[0],a[1],a[2],a[3],t.body.maxDetected,t.body.minConfidence);return Un.inputs[0].shape?Dk(r,[e.shape[1],e.shape[2]],[Un.inputs[0].shape[2],Un.inputs[0].shape[1]]):[]}async function dg(e){return Un?e.debug&&he("cached model:",Un.modelUrl):(Un=await Dt(_t(e.modelBasePath,e.body.modelPath)),!Un||!Un.modelUrl?he("load model failed:",e.body.modelPath):e.debug&&he("load model:",Un.modelUrl)),Un}var gg={};ya(gg,{load:()=>yg,predict:()=>Ag});function R0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function np(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Lk(e,t,n){let a=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/a,e.startPoint[0]/r,e.endPoint[1]/a,e.endPoint[0]/r]];return Ge.cropAndResize(t,s,[0],n)}function Wk(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],a=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:a,palmLandmarks:r,confidence:e.confidence}}function M0(e,t=1.5){let n=np(e),a=R0(e),r=[t*a[0]/2,t*a[1]/2],s=[n[0]-r[0],n[1]-r[1]],i=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function F0(e){let t=np(e),n=R0(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}var 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Ge.nonMaxSuppressionAsync(l,i,n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence),d=u.arraySync();s.dispose(),u.dispose();let p=[];for(let c of d)if(i[c]>=n.hand.minConfidence){let h=Re(l,[c,0],[1,-1]),m=Re(r,[c,5],[1,14]),f=W(()=>this.normalizeLandmarks(m,c).reshape([-1,2]));m.dispose(),p.push({box:h,palmLandmarks:f,confidence:i[c]})}return r.dispose(),l.dispose(),p}async estimateHandBounds(t,n){let a=t.shape[1],r=t.shape[2],s=W(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let l of i){let u=l.box.dataSync(),d=u.slice(0,2),p=u.slice(2,4),c=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(Wk({startPoint:d,endPoint:p,palmLandmarks:c,confidence:l.confidence},[r/this.inputSize,a/this.inputSize]))}return o}};function toe(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Vk(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return toe(n)}var jk=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function es(e,t){let n=0;for(let a=0;ai[0]),a=t.map(i=>i[1]),r=[Math.min(...n),Math.min(...a)],s=[Math.max(...n),Math.max(...a)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let a=t.map(s=>fg([...s,1],n)),r=this.calculateLandmarksBoundingBox(a);return M0(F0(r),aoe)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),a=M0(F0(n),Gk);a.palmLandmarks=[];for(let r=0;r[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=hg(a,[0,0]),u=o.map(h=>[...fg(h,l),h[2]]),d=Hk(r),p=[...np(n),1],c=[es(p,d[0]),es(p,d[1])];return u.map(h=>[h[0]+c[0],h[1]+c[1],h[2]])}async estimateHands(t,n){let a=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await 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0:Ce.height),zt=la.flags.IS_BROWSER?new n9({canvas:gt}):null),!zt)return{tensor:null,canvas:Ce};zt.reset(),zt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&zt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&zt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&zt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&zt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&zt.addFilter("hue",t.filter.hue),t.filter.negative&&zt.addFilter("negative"),t.filter.sepia&&zt.addFilter("sepia"),t.filter.vintage&&zt.addFilter("brownie"),t.filter.sepia&&zt.addFilter("sepia"),t.filter.kodachrome&&zt.addFilter("kodachrome"),t.filter.technicolor&&zt.addFilter("technicolor"),t.filter.polaroid&&zt.addFilter("polaroid"),t.filter.pixelate!==0&&zt.addFilter("pixelate",t.filter.pixelate),zt.apply(Ce)}else gt=Ce,zt&&(zt=null);let u;if(gt.data){let p=[gt.height,gt.width,3];u=xc(gt.data,p,"int32")}else if(gt instanceof ImageData)u=pi.fromPixels(gt);else if(t.backend==="webgl"||t.backend==="humangl"){let p=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");p.width=i,p.height=o;let c=p.getContext("2d");c==null||c.drawImage(gt,0,0),u=pi.fromPixels(p)}else{let p=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");p.width=i,p.height=o;let c=p.getContext("2d");c==null||c.drawImage(gt,0,0);let h=c==null?void 0:c.getImageData(0,0,i,o);u=pi.fromPixels(h)}let d=u.toFloat();n=d.expandDims(0),u.dispose(),d.dispose()}let a=t.filter.return?gt:null;return{tensor:n,canvas:a}}var zg={};ya(zg,{all:()=>coe,body:()=>s9,canvas:()=>poe,face:()=>r9,gesture:()=>a9,hand:()=>i9,object:()=>o9,options:()=>as,person:()=>uoe});var as={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe 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0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let a of t){let r=a[2]||0;e.strokeStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(a[0],Math.round(a[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function ap(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){Dg(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let a=0;a1&&l[1].length>0){let u=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${u}: ${l[1]}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(d,8,2+s*a.lineHeight)),r.fillStyle=a.labelColor,r.fillText(d,6,0+s*a.lineHeight),s+=1}}}async function r9(e,t,n){let a=$n(as,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r)for(let s of t){r.font=a.font,r.strokeStyle=a.color,r.fillStyle=a.color,a.drawBoxes&&(a.useRawBoxes?rs(r,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],a):rs(r,s.box[0],s.box[1],s.box[2],s.box[3],a));let i=[];if(i.push(`face confidence: ${Math.trunc(100*s.confidence)}%`),s.genderConfidence&&i.push(`${s.gender||""} ${Math.trunc(100*s.genderConfidence)}% confident`),s.age&&i.push(`age: ${s.age||""}`),s.iris&&i.push(`iris distance: ${s.iris}`),s.emotion&&s.emotion.length>0){let o=s.emotion.map(l=>`${Math.trunc(100*l.score)}% ${l.emotion}`);i.push(o.join(" "))}s.rotation&&s.rotation.angle&&s.rotation.angle.roll&&i.push(`roll: ${Math.trunc(100*s.rotation.angle.roll)/100} yaw:${Math.trunc(100*s.rotation.angle.yaw)/100} pitch:${Math.trunc(100*s.rotation.angle.pitch)/100}`),i.length===0&&i.push("face"),r.fillStyle=a.color;for(let o=i.length-1;o>=0;o--){let l=Math.max(s.box[0],0),u=o*a.lineHeight+s.box[1];a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(i[o],l+5,u+16)),r.fillStyle=a.labelColor,r.fillText(i[o],l+4,u+15)}if(r.lineWidth=1,s.mesh&&s.mesh.length>0){if(a.drawPoints)for(let o of s.mesh)$g(r,o[0],o[1],o[2],a);if(a.drawPolygons){r.lineWidth=1;for(let o=0;os.mesh[u]);Dg(r,l,a)}if(s.annotations&&s.annotations.leftEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let o=Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0])/2,l=Math.abs(s.annotations.leftEyeIris[4][1]-s.annotations.leftEyeIris[2][1])/2;r.ellipse(s.annotations.leftEyeIris[0][0],s.annotations.leftEyeIris[0][1],o,l,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(s.annotations&&s.annotations.rightEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let o=Math.abs(s.annotations.rightEyeIris[3][0]-s.annotations.rightEyeIris[1][0])/2,l=Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])/2;r.ellipse(s.annotations.rightEyeIris[0][0],s.annotations.rightEyeIris[0][1],o,l,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}}}}}async function s9(e,t,n){var s;let a=$n(as,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round";for(let i=0;iu.part==="leftShoulder"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position.x,o.position.y]),ap(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightHip"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftHip"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position.x,o.position.y]),l.length===4&&Dg(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="leftHip"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftKnee"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftAnkle"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftHeel"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftFoot"),o&&l.push([o.position.x,o.position.y]),ap(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightHip"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightKnee"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightAnkle"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightHeel"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightFoot"),o&&l.push([o.position.x,o.position.y]),ap(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftElbow"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftWrist"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftPalm"),o&&l.push([o.position.x,o.position.y]),ap(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightElbow"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightWrist"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightPalm"),o&&l.push([o.position.x,o.position.y]),ap(r,l,a)}}}}async function i9(e,t,n){let a=$n(as,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s of t){if(a.drawBoxes){r.strokeStyle=a.color,r.fillStyle=a.color;let i;if(!a.calculateHandBox)i=a.useRawBoxes?s.boxRaw:s.box;else if(i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],s.landmarks&&s.landmarks.length>0){for(let o of s.landmarks)o[0]i[2]&&(i[2]=o[0]),o[1]>i[3]&&(i[3]=o[1]);i[2]-=i[0],i[3]-=i[1]}a.useRawBoxes?rs(r,e.width*i[0],e.height*i[1],e.width*i[2],e.height*i[3],a):rs(r,i[0],i[1],i[2],i[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText("hand",i[0]+3,1+i[1]+a.lineHeight,i[2])),r.fillStyle=a.labelColor,r.fillText("hand",i[0]+2,0+i[1]+a.lineHeight,i[2])),r.stroke()}if(a.drawPoints&&s.landmarks&&s.landmarks.length>0)for(let i of s.landmarks)r.fillStyle=a.useDepth?`rgba(${127.5+2*i[2]}, ${127.5-2*i[2]}, 255, 0.5)`:a.color,$g(r,i[0],i[1],0,a);if(a.drawLabels){let i=(o,l)=>{r.fillStyle=a.useDepth?`rgba(${127.5+2*o[o.length-1][2]}, ${127.5-2*o[o.length-1][2]}, 255, 0.5)`:a.color,r.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4)};r.font=a.font,i(s.annotations.indexFinger,"index"),i(s.annotations.middleFinger,"middle"),i(s.annotations.ringFinger,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palmBase,"palm")}if(a.drawPolygons){let i=o=>{if(!!o)for(let l=0;l0?l-1:0][0],o[l>0?l-1:0][1]),r.lineTo(o[l][0],o[l][1]),r.stroke()};r.lineWidth=a.lineWidth,i(s.annotations.indexFinger),i(s.annotations.middleFinger),i(s.annotations.ringFinger),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function o9(e,t,n){let a=$n(as,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s of t)if(a.drawBoxes){if(r.strokeStyle=a.color,r.fillStyle=a.color,a.useRawBoxes?rs(r,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],a):rs(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels){let i=`${Math.round(100*s.score)}% ${s.label}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(i,s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText(i,s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])}r.stroke()}}}async function uoe(e,t,n){let a=$n(as,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s=0;s((t.bufferedFactor-1)*ft.body[n].box[r]+a)/t.bufferedFactor),ft.body[n].boxRaw=e.body[n].boxRaw.map((a,r)=>((t.bufferedFactor-1)*ft.body[n].boxRaw[r]+a)/t.bufferedFactor),ft.body[n].keypoints=e.body[n].keypoints.map((a,r)=>({score:a.score,part:a.part,position:{x:ft.body[n].keypoints[r]?((t.bufferedFactor-1)*ft.body[n].keypoints[r].position.x+a.position.x)/t.bufferedFactor:a.position.x,y:ft.body[n].keypoints[r]?((t.bufferedFactor-1)*ft.body[n].keypoints[r].position.y+a.position.y)/t.bufferedFactor:a.position.y}}));(!ft.hand||e.hand.length!==ft.hand.length)&&(ft.hand=JSON.parse(JSON.stringify(e.hand)));for(let n=0;n((t.bufferedFactor-1)*ft.hand[n].box[s]+r)/t.bufferedFactor),ft.hand[n].boxRaw=e.hand[n].boxRaw.map((r,s)=>((t.bufferedFactor-1)*ft.hand[n].boxRaw[s]+r)/t.bufferedFactor),ft.hand[n].landmarks=e.hand[n].landmarks.map((r,s)=>r.map((i,o)=>((t.bufferedFactor-1)*ft.hand[n].landmarks[s][o]+i)/t.bufferedFactor));let a=Object.keys(e.hand[n].annotations);for(let r of a)ft.hand[n].annotations[r]=e.hand[n].annotations[r].map((s,i)=>s.map((o,l)=>((t.bufferedFactor-1)*ft.hand[n].annotations[r][i][l]+o)/t.bufferedFactor))}}async function poe(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function coe(e,t,n){let a=$n(as,n);!t||!e||e instanceof HTMLCanvasElement&&(a.bufferedOutput?doe(t,a):ft=t,r9(e,t.face,a),s9(e,ft.body,a),i9(e,ft.hand,a),a9(e,t.gesture,a),o9(e,t.object,a))}function l9(e,t,n,a){var i,o,l,u,d,p,c,h,m,f,A,y,g,x,w,b,v,N,T,C,$,z,_,V,U,j,X,G,te,Y,ie,ne,le,Q;let r=0,s=[];for(let ue of e){let J={id:r++,face:ue,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let re of t)ue.box[0]>re.box[0]&&ue.box[0]re.box[1]&&ue.box[1]+ue.box[3]J.body.box[0]&&re.box[0]+re.box[2]J.body.box[1]&&re.box[1]+re.box[3]J.body.box[0]&&re.box[1]+re.box[3]>J.body.box[1]&&re.box[1]+re.box[3]{if(!on(this,rp))return;let n=this.tf.engine().state.numTensors,a=on(this,ru);ga(this,ru,n);let r=n-a;r!==0&&he(...t,r)};Zn(this,_0,t=>{if(!on(this,sp))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Le))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Zn(this,ip,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let a=nt();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&he("running inside web worker"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&he("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&he("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&he(`wasm execution: ${r?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!r&&he("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&fk();try{await this.tf.setBackend(this.config.backend)}catch(r){he("error: cannot set 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t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),n,a;switch(this.config.warmup){case"face":n=await t(z0);break;case"full":n=await t(O0);break;default:n=null}if(n){let r=await createImageBitmap(n);a=await this.detect(r,this.config),r.close()}return a});Zn(this,W0,async()=>new Promise(t=>{let n,a=0;switch(this.config.warmup){case"face":a=256,n="data:image/jpeg;base64,"+z0;break;case"full":case"body":a=1200,n="data:image/jpeg;base64,"+O0;break;default:n=null}let r=new Image;r.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(a,a):document.createElement("canvas");s.width=r.naturalWidth,s.height=r.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(r,0,0);let o=await this.detect(s,this.config);t(o)},n?r.src=n:t(null)}));Zn(this,B0,async()=>{let t=r=>Buffer.from(r,"base64"),n;if(this.config.warmup==="face"&&(n=t(z0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(O0)),!n)return null;let a;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),s=r.expandDims(0);this.tf.dispose(r),a=await this.detect(s,this.config),this.tf.dispose(s)}else this.config.debug&&he("Warmup tfjs-node not loaded");return a});this.tf=Yd,this.draw=zg,this.version=u9,this.config=$n(Kg,t),this.state="idle",ga(this,ru,0),ga(this,rp,!1),ga(this,sp,!1),ga(this,Wi,!0),ga(this,su,0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null},this.image=n=>Fg(n,this.config),this.classes={facemesh:q2,emotion:Y2,faceres:ng,body:this.config.body.modelPath.includes("posenet")?pg:vg,hand:gg,nanodet:Ng,centernet:Mg},this.faceTriangulation=Nk,this.faceUVMap=Tk,this.sysinfo=Zg(),ga(this,Bi,1)}similarity(t,n){return eg(t,n)}enhance(t){return tg(t)}match(t,n,a=0){return Rk(t,n,a)}async load(t={}){this.state="load";let n=nt();t&&(this.config=$n(this.config,t)),on(this,Wi)&&(this.config.debug&&he(`version: ${this.version}`),this.config.debug&&he(`tfjs version: ${this.tf.version_core}`),this.config.debug&&he("platform:",this.sysinfo.platform),this.config.debug&&he("agent:",this.sysinfo.agent),await on(this,ip).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&he("configuration:",this.config),this.config.debug&&he("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.emotion,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.nanodet,this.models.centernet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?G2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?Z2(this.config):null),this.models.handpose||(this.config.hand.enabled?yg(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?dg(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?xg(this.config):null),this.models.nanodet||(this.config.object.enabled&&this.config.object.modelPath.includes("nanodet")?Ig(this.config):null),this.models.centernet||(this.config.object.enabled&&this.config.object.modelPath.includes("centernet")?Cg(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?Q2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await G2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await Z2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await yg(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await dg(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await xg(this.config)),this.config.object.enabled&&!this.models.nanodet&&this.config.object.modelPath.includes("nanodet")&&(this.models.nanodet=await Ig(this.config)),this.config.object.enabled&&!this.models.centernet&&this.config.object.modelPath.includes("centernet")&&(this.models.centernet=await Cg(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await Q2(this.config))),on(this,Wi)&&(this.config.debug&&he("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),ga(this,Wi,!1));let a=Math.trunc(nt()-n);a>(this.perf.load||0)&&(this.perf.load=a)}async detect(t,n={}){return new Promise(async a=>{this.state="config";let r;this.config=$n(this.config,n),this.state="check";let s=on(this,_0).call(this,t);s&&(he(s,t),a({error:s}));let i=nt();await on(this,ip).call(this),await this.load(),r=nt();let o=Fg(t,this.config);if(!o||!o.tensor){he("could not convert input to tensor"),a({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(nt()-r),this.analyze("Get Image:"),r=nt(),this.config.skipFrame=await on(this,P0).call(this,o.tensor),this.perf.frames||(this.perf.frames=0),this.perf.cached||(this.perf.cached=0),this.perf.frames++,this.config.skipFrame&&this.perf.cached++,this.perf.changed=Math.trunc(nt()-r),this.analyze("Check Changed:");let l,u,d,p,c;this.config.async?(l=this.config.face.enabled?ag(this,o.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",r=nt(),l=this.config.face.enabled?await ag(this,o.tensor):[],c=Math.trunc(nt()-r),c>0&&(this.perf.face=c)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?u=this.config.body.enabled?ug(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")&&(u=this.config.body.enabled?bg(o.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",r=nt(),this.config.body.modelPath.includes("posenet")?u=this.config.body.enabled?await ug(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")&&(u=this.config.body.enabled?await bg(o.tensor,this.config):[]),c=Math.trunc(nt()-r),c>0&&(this.perf.body=c)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(d=this.config.hand.enabled?Ag(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",r=nt(),d=this.config.hand.enabled?await Ag(o.tensor,this.config):[],c=Math.trunc(nt()-r),c>0&&(this.perf.hand=c)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?p=this.config.object.enabled?Sg(o.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(p=this.config.object.enabled?Rg(o.tensor,this.config):[]),this.perf.object&&delete this.perf.object):(this.state="run:object",r=nt(),this.config.object.modelPath.includes("nanodet")?p=this.config.object.enabled?await Sg(o.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(p=this.config.object.enabled?await Rg(o.tensor,this.config):[]),c=Math.trunc(nt()-r),c>0&&(this.perf.object=c)),this.analyze("End Object:"),this.config.async&&([l,u,d,p]=await Promise.all([l,u,d,p])),Ee(o.tensor);let h=[];this.config.gesture.enabled&&(r=nt(),h=[...Qk(l),...Jk(u),...t9(d),...e9(l)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(nt()-r)),this.perf.total=Math.trunc(nt()-i),this.state="idle";let m={face:l,body:u,hand:d,gesture:h,object:p,performance:this.perf,canvas:o.canvas,timestamp:Date.now(),get persons(){return l9(l,u,d,h)}};a(m)})}async warmup(t={}){let n=nt();if(t&&(this.config=$n(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let a;typeof createImageBitmap=="function"?a=await on(this,L0).call(this):typeof Image!="undefined"?a=await on(this,W0).call(this):a=await on(this,B0).call(this);let r=nt();return this.config.debug&&he("Warmup",this.config.warmup,Math.round(r-n),"ms",a),a}};ru=new WeakMap,rp=new WeakMap,sp=new WeakMap,Wi=new WeakMap,Bi=new WeakMap,su=new WeakMap,_0=new WeakMap,ip=new WeakMap,P0=new WeakMap,L0=new WeakMap,W0=new WeakMap,B0=new WeakMap;return foe;})(); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the License); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an AS IS BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * https://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** @license See the LICENSE file. */